This page has only limited features, please log in for full access.
The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018–2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.
Wei-Jen Chen; Mao-Jhen Jhou; Tian-Shyug Lee; Chi-Jie Lu. Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association. Entropy 2021, 23, 477 .
AMA StyleWei-Jen Chen, Mao-Jhen Jhou, Tian-Shyug Lee, Chi-Jie Lu. Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association. Entropy. 2021; 23 (4):477.
Chicago/Turabian StyleWei-Jen Chen; Mao-Jhen Jhou; Tian-Shyug Lee; Chi-Jie Lu. 2021. "Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association." Entropy 23, no. 4: 477.
Background The relationship between sleep duration and physical fitness is one aspect of sleep health. Potential factors associated with sleep duration interfere with physical fitness performance, but the impact trends on physical fitness indicators remain unclear. Methods This study examined associations between sleep duration and physical fitness among young to middle-aged adults in Taiwan. A total of 42,781 Taiwanese adults aged 23–45 participated in the National Physical Fitness Examination Survey 2013 (NPFES-2013) in Taiwan between October 2013 and March 2014. A standardized structural questionnaire was used to record participants’ sleep duration, which was stratified as short (< 6 h/day (h/d)), moderate (6–7 h/d; 7–8 h/d; 8-9 h), and long (≥ 9 h/d) sleep duration groups. Physical fitness was assessed based on four components: body composition (body mass index [BMI], waist-to-height ratio [WHtR], and waist-to-hip ratio [WHR]), muscle strength and endurance (1-min bent-leg sit-up test [BS]), flexibility (sit-and-reach test [SR]), and cardiorespiratory endurance index (3-min step test [CEI]). Results By using analysis of covariance (ANCOVA), after sex grouping and age adjustment, we observed that sleep duration was significantly associated with obesity, functional fitness, and self-perception of health. The sleep duration for low obesity-related values (BMI, WHtR, and WHR) for men was 7–9 h/d, and that for women was 7–8 h/d. Sleeping more than 8 h/d showed poor functional fitness performances (BS and SR). For both sexes, sleep duration of 8–9 h/d was the optimal sleep duration for self-perceptions of health. Conclusions Our research found that there were wide and different associations of sleep duration with physical fitness and self-perception of health among Taiwanese adults aged 23–45, and there were differences in these associated manifestations between men and women. This study could be of great importance in regional public health management in Taiwan, and provide inspirations for clinical research on physical fitness.
Ming Gu; Chia-Chen Liu; Chi-Chieh Hsu; Chi-Jie Lu; Tian-Shyug Lee; Mingchih Chen; Chien-Chang Ho. Associations of sleep duration with physical fitness performance and self-perception of health: a cross-sectional study of Taiwanese adults aged 23–45. BMC Public Health 2021, 21, 1 -8.
AMA StyleMing Gu, Chia-Chen Liu, Chi-Chieh Hsu, Chi-Jie Lu, Tian-Shyug Lee, Mingchih Chen, Chien-Chang Ho. Associations of sleep duration with physical fitness performance and self-perception of health: a cross-sectional study of Taiwanese adults aged 23–45. BMC Public Health. 2021; 21 (1):1-8.
Chicago/Turabian StyleMing Gu; Chia-Chen Liu; Chi-Chieh Hsu; Chi-Jie Lu; Tian-Shyug Lee; Mingchih Chen; Chien-Chang Ho. 2021. "Associations of sleep duration with physical fitness performance and self-perception of health: a cross-sectional study of Taiwanese adults aged 23–45." BMC Public Health 21, no. 1: 1-8.
The safety and health of homeless people are important social issues. Metabolic syndrome (MetS) is a sub-health-risk phenomenon that has been severely aggravated worldwide in recent years. The purpose of this study was to investigate the prevalence and risk factors of MetS among the homeless in Taipei City, Taiwan. In this study, a convenience sampling was conducted at homeless counseling agencies in Taipei City from April 2018 to September 2018. A total of 297 homeless participants were recruited, from whom clinical indicators and questionnaire information were collected. Through statistical verification, analysis of variance (ANOVA), and logistic regression, we found the following main conclusions for homeless adults in Taipei: (1) The prevalence of MetS was estimated to be 53%, with 50% meeting four or more diagnostic conditions. (2) Dyslipidemia (high-density lipoprotein (HDL) deficiency and elevated triglyceride (TG)) showed the strongest association with the prevalence of MetS; more than 83% of people with HDL deficiency or hypertriglyceridemia had MetS. For the patient groups meeting more MetS diagnostic conditions, the values of high-density lipoprotein cholesterol (HDL-C), TG, and total cholesterol (TC) increased significantly. (3) The deterioration of MetS was significantly related to the high prevalence of hyperlipidemia (HL). (4) The homeless who were divorced, separated or widowed were more likely to suffer from MetS.
Ming Gu; Chi-Jie Lu; Tian-Shyug Lee; Mingchih Chen; Chih-Kuang Liu; Ching-Lin Chen. Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study. International Journal of Environmental Research and Public Health 2021, 18, 1716 .
AMA StyleMing Gu, Chi-Jie Lu, Tian-Shyug Lee, Mingchih Chen, Chih-Kuang Liu, Ching-Lin Chen. Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study. International Journal of Environmental Research and Public Health. 2021; 18 (4):1716.
Chicago/Turabian StyleMing Gu; Chi-Jie Lu; Tian-Shyug Lee; Mingchih Chen; Chih-Kuang Liu; Ching-Lin Chen. 2021. "Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study." International Journal of Environmental Research and Public Health 18, no. 4: 1716.
Atropine is a common treatment used in children with myopia. However, it probably affects intraocular pressure (IOP) under some conditions. Our research aims to analyze clinical data by using machine learning models to evaluate the effect of 19 important factors on intraocular pressure (IOP) in children with myopia treated with topical atropine. The data is collected on 1545 eyes with spherical equivalent (SE) less than −10.0 diopters (D) treated with atropine for myopia control. Four machine learning models, namely multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost), were used. Linear regression (LR) was used for benchmarking. The 10-fold cross-validation method was used to estimate the performance of the five methods. The main outcome measure is that the 19 important factors associated with atropine use that may affect IOP are evaluated using machine learning models. Endpoint IOP at the last visit was set as the target variable. The results show that the top five significant variables, including baseline IOP, recruitment duration, age, total duration and previous cumulative dosage, were identified as most significant for evaluating the effect of atropine use for treating myopia on IOP. We can conclude that the use of machine learning methods to evaluate factors that affect IOP in children with myopia treated with topical atropine is promising. XGBoost is the best predictive model, and baseline IOP is the most accurate predictive factor for endpoint IOP among all machine learning approaches.
Tzu-En Wu; Hsin-An Chen; Mao-Jhen Jhou; Yen-Ning Chen; Ting-Jen Chang; Chi-Jie Lu. Evaluating the Effect of Topical Atropine Use for Myopia Control on Intraocular Pressure by Using Machine Learning. Journal of Clinical Medicine 2020, 10, 111 .
AMA StyleTzu-En Wu, Hsin-An Chen, Mao-Jhen Jhou, Yen-Ning Chen, Ting-Jen Chang, Chi-Jie Lu. Evaluating the Effect of Topical Atropine Use for Myopia Control on Intraocular Pressure by Using Machine Learning. Journal of Clinical Medicine. 2020; 10 (1):111.
Chicago/Turabian StyleTzu-En Wu; Hsin-An Chen; Mao-Jhen Jhou; Yen-Ning Chen; Ting-Jen Chang; Chi-Jie Lu. 2020. "Evaluating the Effect of Topical Atropine Use for Myopia Control on Intraocular Pressure by Using Machine Learning." Journal of Clinical Medicine 10, no. 1: 111.
This paper investigates a multistage production–inventory model for deteriorating items, including raw materials and finished goods, based on collaborative preservation technology investment, hitherto not treated in the previous researches. The major purpose is to determine the optimal materials supply, production delivery, replenishment and investment policies for maximizing the joint total profit of the integrated system. Considering the proposed model, this paper uses mathematical programming analysis to ascertain the optimal solutions. Furthermore, several numerical examples are presented to demonstrate the solution process and verify the concavity of the proposed model. Sensitivity analyses with respect to major parameters are also performed. The numerical results shows that market demand, fixed shipping cost, production rate, manufacturer’s sales price and holding cost of finished goods may affect the optimal number of shipments. Besides, when collaborative preservation technology investment becomes an option, whether the effect of the deterioration rate of raw materials or finished goods on the shipping and ordering quantity will be reduced by preservation technology investment. Finally, the increase in the amount of raw materials used to produce a finished product implies the amount of finished goods produced by the original material quantity is reduced, so the preservation technology investment will be increased.
Chi-Chang Chang; Chi-Jie Lu; Chih-Te Yang. Multistage supply chain production–inventory model with collaborative preservation technology investment. Scientia Iranica 2020, 1 .
AMA StyleChi-Chang Chang, Chi-Jie Lu, Chih-Te Yang. Multistage supply chain production–inventory model with collaborative preservation technology investment. Scientia Iranica. 2020; ():1.
Chicago/Turabian StyleChi-Chang Chang; Chi-Jie Lu; Chih-Te Yang. 2020. "Multistage supply chain production–inventory model with collaborative preservation technology investment." Scientia Iranica , no. : 1.
This paper investigated a multistage sustainable production–inventory model for deteriorating items (i.e., raw materials and finished goods) with price-dependent demand and collaborative carbon reduction technology investment under carbon tax regulation. The model was developed by first defining the total profit of the supply chain members under carbon tax regulation and, second, considering a manufacturer (leader)–retailer (follower) Stackelberg game. The optimal equilibrium solutions that maximize the manufacturer’s and retailer’s total profits were determined through the method analysis. An algorithm complemented the model to determine the optimal equilibrium solutions, which were then treated with sensitivity analyses for the major parameters. Based on the numerical analysis, (a) carbon tax policies help reduce carbon emissions for both the manufacturer and retailer; (b) most carbon emissions from supply chain operations negatively impact the total profits of both members; (c) the retailer may increase the optimal equilibrium selling price to respond to an increase in carbon emissions from supply chain operations or carbon tax; and (d) autonomous consumption positively affects both members’ optimal equilibrium policies and total profits, whereas induced consumption does the opposite. These findings are very managerial and instructive for companies seeking profits and fulfilling environmental responsibility and governments.
Chi-Jie Lu; Tian-Shyug Lee; Ming Gu; Chih-Te Yang. A Multistage Sustainable Production–Inventory Model with Carbon Emission Reduction and Price-Dependent Demand under Stackelberg Game. Applied Sciences 2020, 10, 4878 .
AMA StyleChi-Jie Lu, Tian-Shyug Lee, Ming Gu, Chih-Te Yang. A Multistage Sustainable Production–Inventory Model with Carbon Emission Reduction and Price-Dependent Demand under Stackelberg Game. Applied Sciences. 2020; 10 (14):4878.
Chicago/Turabian StyleChi-Jie Lu; Tian-Shyug Lee; Ming Gu; Chih-Te Yang. 2020. "A Multistage Sustainable Production–Inventory Model with Carbon Emission Reduction and Price-Dependent Demand under Stackelberg Game." Applied Sciences 10, no. 14: 4878.
Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.
Chin-Chuan Shih; Chi-Jie Lu; Gin-Den Chen; Chi-Chang Chang. Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals. International Journal of Environmental Research and Public Health 2020, 17, 4973 .
AMA StyleChin-Chuan Shih, Chi-Jie Lu, Gin-Den Chen, Chi-Chang Chang. Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals. International Journal of Environmental Research and Public Health. 2020; 17 (14):4973.
Chicago/Turabian StyleChin-Chuan Shih; Chi-Jie Lu; Gin-Den Chen; Chi-Chang Chang. 2020. "Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals." International Journal of Environmental Research and Public Health 17, no. 14: 4973.
Influenza is a serious public health issue, as it can cause acute suffering and even death, social disruption, and economic loss. Effective forecasting of influenza outpatient visits is beneficial to anticipate and prevent medical resource shortages. This study uses regional data on influenza outpatient visits to propose a two-dimensional hierarchical decision tree scheme for forecasting influenza outpatient visits. The Taiwan weekly influenza outpatient visit data were collected from the national infectious disease statistics system and used for an empirical example. The 788 data points start in the first week of 2005 and end in the second week of 2020. The empirical results revealed that the proposed forecasting scheme outperformed five competing models and was able to forecast one to four weeks of anticipated influenza outpatient visits. The scheme may be an effective and promising alternative for forecasting one to four steps (weeks) ahead of nationwide influenza outpatient visits in Taiwan. Our results also suggest that, for forecasting nationwide influenza outpatient visits in Taiwan, one- and two-time lag information and regional information from the Taipei, North, and South regions are significant.
Tian-Shyug Lee; I-Fei Chen; Ting-Jen Chang; Chi-Jie Lu. Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme. International Journal of Environmental Research and Public Health 2020, 17, 4743 .
AMA StyleTian-Shyug Lee, I-Fei Chen, Ting-Jen Chang, Chi-Jie Lu. Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme. International Journal of Environmental Research and Public Health. 2020; 17 (13):4743.
Chicago/Turabian StyleTian-Shyug Lee; I-Fei Chen; Ting-Jen Chang; Chi-Jie Lu. 2020. "Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme." International Journal of Environmental Research and Public Health 17, no. 13: 4743.
Greenhouse gases (mainly carbon dioxide) is one of the important factors contributing to extreme weather we face today. The carbon cap-and trade and carbon offset are common but important carbon emission reduction policies in many countries. Further, carbon emissions generated by corporate activities can be effectively reduced through specific capital investment in green technology. However, such kind of capital investment is rather costly and is unlikely for a single company to solely invest in it. Moreover, most decision-making situations in business are correlated instead of independent. Therefore, this paper explores potential competitive and cooperative issues of the sustainable product-inventory models with collaborative investment in carbon emission reduction technology under carbon cap-and trade and carbon offset polies. A Stackelberg approach of game theory is utilized for determining the optimal equilibrium solution between the buyer and the vendor under different carbon emission reductions. Realistic data examples are used to demonstrate the solution and sensitivity analysis on the main variables. The results indicate that proportions of investment by the vendor and buyer in carbon emission reduction technology play a critical role in both parties’ shipping, ordering strategies and profits. This role becomes more prominent as the proportion of investment increases. In addition, an increased proportion of investment in carbon emission reduction technology involves increased investment and thus contributes to the fulfillment of the carbon reduction goal. Finally, a comparison between the carbon cap-and-trade and carbon offset policies reveals that, although increases in the carbon trading price and carbon offset price are both conducive to carbon emissions inhibition, they exert different effects on the total profits of the vendor and buyer.
Chi-Jie Lu; Chih-Te Yang; Hsiu-Feng Yen. Stackelberg game approach for sustainable production-inventory model with collaborative investment in technology for reducing carbon emissions. Journal of Cleaner Production 2020, 270, 121963 .
AMA StyleChi-Jie Lu, Chih-Te Yang, Hsiu-Feng Yen. Stackelberg game approach for sustainable production-inventory model with collaborative investment in technology for reducing carbon emissions. Journal of Cleaner Production. 2020; 270 ():121963.
Chicago/Turabian StyleChi-Jie Lu; Chih-Te Yang; Hsiu-Feng Yen. 2020. "Stackelberg game approach for sustainable production-inventory model with collaborative investment in technology for reducing carbon emissions." Journal of Cleaner Production 270, no. : 121963.
Physical fitness is a powerful indicator of health. Sleep condition plays an essential role in maintaining quality of life and is an important marker that predicts physical fitness. This study aimed to determine the relationship between sleep conditions (sleep quality, sleep duration, bedtime) and multiple physical fitness indicators (body mass index (BMI), flexibility, abdominal muscle strength and endurance, cardiopulmonary endurance) in a well-characterized population of Taiwanese adults aged 23 to 65. The applied data were obtained from the National Physical Fitness Examination Survey 2014 conducted in Taiwan. We assessed the association of the sleep conditions with physical fitness performances in Taiwanese adults by using the multivariate adaptive regression spline (MARS) method with a total of 69,559 samples. The results show that sleep duration, sleep quality, and bedtime were statistically significant influence factors on physical fitness performances with different degrees. Gender was an important factor that affects the effects of daily sleep conditions on performances of physical fitness. Sleep duration was the most important factor as it was simultaneously correlated with BMI, sit-ups, and sit-and-reach indicators in both genders. Bedtime and sleep quality were only associated with sit-ups performance in both genders.
Chi-Chieh Hsu; Ming Gu; Tian-Shyug Lee; Chi-Jie Lu. The Effects of Daily Sleep Condition on Performances of Physical Fitness among Taiwanese Adults: A Cross-Sectional Study. International Journal of Environmental Research and Public Health 2020, 17, 1907 .
AMA StyleChi-Chieh Hsu, Ming Gu, Tian-Shyug Lee, Chi-Jie Lu. The Effects of Daily Sleep Condition on Performances of Physical Fitness among Taiwanese Adults: A Cross-Sectional Study. International Journal of Environmental Research and Public Health. 2020; 17 (6):1907.
Chicago/Turabian StyleChi-Chieh Hsu; Ming Gu; Tian-Shyug Lee; Chi-Jie Lu. 2020. "The Effects of Daily Sleep Condition on Performances of Physical Fitness among Taiwanese Adults: A Cross-Sectional Study." International Journal of Environmental Research and Public Health 17, no. 6: 1907.
Colorectal cancer is ranked third and fourth in terms of mortality and cancer incidence in the world. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five machine learning classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. A total of 4287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.
Wen-Chien Ting; Horng-Rong Chang; Chi-Chang Chang; Chi-Jie Lu. Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors. Applied Sciences 2020, 10, 1355 .
AMA StyleWen-Chien Ting, Horng-Rong Chang, Chi-Chang Chang, Chi-Jie Lu. Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors. Applied Sciences. 2020; 10 (4):1355.
Chicago/Turabian StyleWen-Chien Ting; Horng-Rong Chang; Chi-Chang Chang; Chi-Jie Lu. 2020. "Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors." Applied Sciences 10, no. 4: 1355.
Ming-Chih Chen; Tian-Shyug Lee; Chi-Jie Lu; Chien-Chang Ho; Ming Gu. Physical Fitness and Happiness Research in Taiwanese Adults. Proceedings of the third International Conference on Medical and Health Informatics 2019 - ICMHI 2019 2019, 1 .
AMA StyleMing-Chih Chen, Tian-Shyug Lee, Chi-Jie Lu, Chien-Chang Ho, Ming Gu. Physical Fitness and Happiness Research in Taiwanese Adults. Proceedings of the third International Conference on Medical and Health Informatics 2019 - ICMHI 2019. 2019; ():1.
Chicago/Turabian StyleMing-Chih Chen; Tian-Shyug Lee; Chi-Jie Lu; Chien-Chang Ho; Ming Gu. 2019. "Physical Fitness and Happiness Research in Taiwanese Adults." Proceedings of the third International Conference on Medical and Health Informatics 2019 - ICMHI 2019 , no. : 1.
The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.
Yuehjen E. Shao; Po-Yu Chang; Chi-Jie Lu. Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process. Complexity 2017, 2017, 1 -10.
AMA StyleYuehjen E. Shao, Po-Yu Chang, Chi-Jie Lu. Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process. Complexity. 2017; 2017 ():1-10.
Chicago/Turabian StyleYuehjen E. Shao; Po-Yu Chang; Chi-Jie Lu. 2017. "Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process." Complexity 2017, no. : 1-10.
Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory.
Chih-Jen Tseng; Chi-Jie Lu; Chi-Chang Chang; Gin-Den Chen; Chalong Cheewakriangkrai. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artificial Intelligence in Medicine 2017, 78, 47 -54.
AMA StyleChih-Jen Tseng, Chi-Jie Lu, Chi-Chang Chang, Gin-Den Chen, Chalong Cheewakriangkrai. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artificial Intelligence in Medicine. 2017; 78 ():47-54.
Chicago/Turabian StyleChih-Jen Tseng; Chi-Jie Lu; Chi-Chang Chang; Gin-Den Chen; Chalong Cheewakriangkrai. 2017. "Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence." Artificial Intelligence in Medicine 78, no. : 47-54.
Sales forecasting has long been crucial for companies since it is important for financial planning, inventory management, marketing, and customer service. In this study, a novel clustering-based sales forecasting scheme that uses an extreme learning machine (ELM) and assembles the results of linkage methods is proposed. The proposed scheme first uses the K-means algorithm to divide the training sales data into multiple disjointed clusters. Then, for each cluster, the ELM is applied to construct a forecasting model. Finally, a test datum is assigned to the most suitable cluster identified according to the result of combining five linkage methods. The constructed ELM model corresponding to the identified cluster is utilized to perform the final prediction. Two real sales datasets of computer servers collected from two multinational electronics companies are used to illustrate the proposed model. Empirical results showed that the proposed clustering-based sales forecasting scheme statistically outperforms eight benchmark models, and hence demonstrates that the proposed approach is an effective alternative for sales forecasting.
Chi-Jie Lu; Ling-Jing Kao. A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server. Engineering Applications of Artificial Intelligence 2016, 55, 231 -238.
AMA StyleChi-Jie Lu, Ling-Jing Kao. A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server. Engineering Applications of Artificial Intelligence. 2016; 55 ():231-238.
Chicago/Turabian StyleChi-Jie Lu; Ling-Jing Kao. 2016. "A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server." Engineering Applications of Artificial Intelligence 55, no. : 231-238.
Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and machine-learning methods is proposed for computer retailing sales forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained forecasting model of the cluster was adopted for sales forecasting. Since the sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based forecasting models were proposed. Real-life sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior forecasting performance for all three products compared with the other five forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based sales forecasting model for computer retailing.
I-Fei Chen; Chi-Jie Lu. Sales forecasting by combining clustering and machine-learning techniques for computer retailing. Neural Computing and Applications 2016, 28, 2633 -2647.
AMA StyleI-Fei Chen, Chi-Jie Lu. Sales forecasting by combining clustering and machine-learning techniques for computer retailing. Neural Computing and Applications. 2016; 28 (9):2633-2647.
Chicago/Turabian StyleI-Fei Chen; Chi-Jie Lu. 2016. "Sales forecasting by combining clustering and machine-learning techniques for computer retailing." Neural Computing and Applications 28, no. 9: 2633-2647.
In this study, a clustering-based sales forecasting scheme based on support vector regression (SVR) is proposed. The proposed scheme first uses k-means algorithm to partition the whole training sales data into several disjoint clusters. Then, for each group, the SVR is applied to construct forecasting model. Finally, for a given testing data, three similarity measurements are used to find the cluster which the testing data belongs to and then employee the corresponding trained SVR model to generate prediction result. A real aggregate sales data of computer server is used as an illustrative example to evaluate the performance of the proposed model. Experimental results revealed that the proposed clustering-based sales forecasting scheme outperforms the single SVR without data clustering and hence is an effective alternative for computer server sales forecasting.
Wenseng Dai; Yang-Yu Chuang; Chi-Jie Lu. A Clustering-based Sales Forecasting Scheme Using Support Vector Regression for Computer Server. Procedia Manufacturing 2015, 2, 82 -86.
AMA StyleWenseng Dai, Yang-Yu Chuang, Chi-Jie Lu. A Clustering-based Sales Forecasting Scheme Using Support Vector Regression for Computer Server. Procedia Manufacturing. 2015; 2 ():82-86.
Chicago/Turabian StyleWenseng Dai; Yang-Yu Chuang; Chi-Jie Lu. 2015. "A Clustering-based Sales Forecasting Scheme Using Support Vector Regression for Computer Server." Procedia Manufacturing 2, no. : 82-86.
Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.
Chi-Jie Lu; Chi-Chang Chang. A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression. The Scientific World Journal 2014, 2014, 1 -8.
AMA StyleChi-Jie Lu, Chi-Chang Chang. A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression. The Scientific World Journal. 2014; 2014 (4):1-8.
Chicago/Turabian StyleChi-Jie Lu; Chi-Chang Chang. 2014. "A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression." The Scientific World Journal 2014, no. 4: 1-8.
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Wensheng Dai; Jui-Yu Wu; Chi-Jie Lu. Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting. The Scientific World Journal 2014, 2014, 1 -9.
AMA StyleWensheng Dai, Jui-Yu Wu, Chi-Jie Lu. Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting. The Scientific World Journal. 2014; 2014 ():1-9.
Chicago/Turabian StyleWensheng Dai; Jui-Yu Wu; Chi-Jie Lu. 2014. "Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting." The Scientific World Journal 2014, no. : 1-9.
Crude oil is the most important nonrenewable energy resource and the most key element for the world. In contrast to typical forecasts of oil price, this study aims at forecasting the demand of imported crude oil (ICO). This study proposes different single stage and two-stage hybrid stages of forecasting models for prediction of ICO in Taiwan. The single stage forecasting modeling includes multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANN), and extreme learning machine (ELM) approaches. While the first step of the two-stage modeling is to select the fewer but more significant explanatory variables, the second step is to generate predictions by using these significant explanatory variables. The proposed two-stage hybrid models consist of integration of different modeling components. Mean absolute percentage error, root mean square error, and mean absolute difference are utilized as the performance measures. Real data set of crude oil in Taiwan for the period of 19932010 and twenty-three associated explanatory variables are sampled and investigated. The forecasting results reveal that the proposed two-stage hybrid modeling is able to accurately predict the demand of crude oil in Taiwan.
Yuehjen E. Shao; Chi-Jie Lu; Chia-Ding Hou. Hybrid Soft Computing Schemes for the Prediction of Import Demand of Crude Oil in Taiwan. Mathematical Problems in Engineering 2014, 2014, 1 -11.
AMA StyleYuehjen E. Shao, Chi-Jie Lu, Chia-Ding Hou. Hybrid Soft Computing Schemes for the Prediction of Import Demand of Crude Oil in Taiwan. Mathematical Problems in Engineering. 2014; 2014 (1):1-11.
Chicago/Turabian StyleYuehjen E. Shao; Chi-Jie Lu; Chia-Ding Hou. 2014. "Hybrid Soft Computing Schemes for the Prediction of Import Demand of Crude Oil in Taiwan." Mathematical Problems in Engineering 2014, no. 1: 1-11.