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Prof. Chun-Che Huang
National Chi Nan University

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0 Business Analytics
0 Knowledge Management
0 rough set
0 rough set theory
0 big data analysis and mining

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rough set theory

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No. 1, University Road, Pu-Li, Nan-Tau 545, Taiwan

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Editorial
Published: 15 March 2021 in Applied Soft Computing
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ACS Style

Ching-Ter Chang; Chun-Che Huang; Tzu-Liang (Bill) Tseng. Preface: Data-driven Decision Making - Theory, Methods, and Applications. Applied Soft Computing 2021, 102, 107261 .

AMA Style

Ching-Ter Chang, Chun-Che Huang, Tzu-Liang (Bill) Tseng. Preface: Data-driven Decision Making - Theory, Methods, and Applications. Applied Soft Computing. 2021; 102 ():107261.

Chicago/Turabian Style

Ching-Ter Chang; Chun-Che Huang; Tzu-Liang (Bill) Tseng. 2021. "Preface: Data-driven Decision Making - Theory, Methods, and Applications." Applied Soft Computing 102, no. : 107261.

Journal article
Published: 26 November 2020 in Applied Sciences
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In recent years, with the development of Web2.0, enterprises, government agencies, and traditional news media, which have been positively influenced by opinion leaders, have been dedicated to understanding leaders’ opinions on the web in order to seek convergence. Specifically, with the increase of environmental awareness, the introduction of green energy and carbon reduction technology has become an important issue. Consequently, studies identifying opinion leaders and followers who are interested in green energy and low carbon have become important. This study aims to find a solution that can identify the characteristics of opinion leaders and followers that can be widely used, which will help certain public policies or issues to be more effectively disseminated in the future. To model the characteristics of opinion leaders and their influence on followers, this study uses a dual matrix. The interaction patterns are recognized among opinion leaders and followers, with the aim of developing public policy to promote green energy and low carbon emissions. A case is studied to validate the superiority of the proposed solution approach. With the proposed approach, a (business) organization can identify and access opinion leaders and their followers. Through communication, these organizations can absorb strain and preserve functions despite the presence of adversity. This study also clearly demonstrates its contribution and novelty through comparisons with the existing alternative method.

ACS Style

Chun-Che Huang; Wen-Yau Liang; Po-An Chen; Yi-Chin Chan. Identification of Opinion Leaders and Followers—A Case Study of Green Energy and Low Carbons. Applied Sciences 2020, 10, 8416 .

AMA Style

Chun-Che Huang, Wen-Yau Liang, Po-An Chen, Yi-Chin Chan. Identification of Opinion Leaders and Followers—A Case Study of Green Energy and Low Carbons. Applied Sciences. 2020; 10 (23):8416.

Chicago/Turabian Style

Chun-Che Huang; Wen-Yau Liang; Po-An Chen; Yi-Chin Chan. 2020. "Identification of Opinion Leaders and Followers—A Case Study of Green Energy and Low Carbons." Applied Sciences 10, no. 23: 8416.

Journal article
Published: 29 September 2020 in Sustainability
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More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment.

ACS Style

Chun-Che Huang; Wen-Yau Liang; Shian-Hua Lin; Tzu-Liang (Bill) Tseng; Yu-Hsien Wang; Kuo-Hsin Wu. Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy. Sustainability 2020, 12, 8057 .

AMA Style

Chun-Che Huang, Wen-Yau Liang, Shian-Hua Lin, Tzu-Liang (Bill) Tseng, Yu-Hsien Wang, Kuo-Hsin Wu. Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy. Sustainability. 2020; 12 (19):8057.

Chicago/Turabian Style

Chun-Che Huang; Wen-Yau Liang; Shian-Hua Lin; Tzu-Liang (Bill) Tseng; Yu-Hsien Wang; Kuo-Hsin Wu. 2020. "Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy." Sustainability 12, no. 19: 8057.

Journal article
Published: 30 August 2020 in Renewable and Sustainable Energy Reviews
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Taiwan highly relies on foreign countries' supply in various types of energy because of the shortage of energy resources. Taiwanese government encourages industrial and academic research organizations to invest research in energy conservation that could be applicable to countries with similar settings. This research analyzed energy conservation and demographic data of households in Taiwan and developed a novel method for best setting household energy conservation strategies. Through the analysis of high volume and high dimensional data, it showed a hybrid analytical method is required, to predict and identify target households and refine their energy conservation strategies. The hybrid method includes a Logistic Regression based predictive model and a Rough Set Theory-based decision-making model. A hybrid analytical approach is used to identify target households and the critical energy conservation factors by households, such as the use of air conditioning in terms of time length and time of the day, age of the house, energy conservation of the electrical appliances, and price promotion. References show that no study was conducted with such a hybrid approach that can refine strategies for the households that were false-positive in achieving the conservation goal. Besides the above-mentioned purpose, this novel method intended to lower the promotion cost, as promotion is a part of the strategy refinement. In overall, the following problems were considered for decision-making: 1. how to identify the target households that require strategy refinement, 2. how to identify key variables and determine the change of these variables to increase the likelihood of achieving conservation goal, 3. what options can be provided to the decision-makers for feasible strategy refinements. Some studies with qualitative approaches and judgmental decision-making methods were found in this area. However, with the available large dataset, the proposed quantitative method achieved better decision-making efficiency and result optimality.

ACS Style

Roger R. Gung; Chun-Che Huang; Wen-I Hung; Yu-Jie Fang. The use of hybrid analytics to establish effective strategies for household energy conservation. Renewable and Sustainable Energy Reviews 2020, 133, 110295 .

AMA Style

Roger R. Gung, Chun-Che Huang, Wen-I Hung, Yu-Jie Fang. The use of hybrid analytics to establish effective strategies for household energy conservation. Renewable and Sustainable Energy Reviews. 2020; 133 ():110295.

Chicago/Turabian Style

Roger R. Gung; Chun-Che Huang; Wen-I Hung; Yu-Jie Fang. 2020. "The use of hybrid analytics to establish effective strategies for household energy conservation." Renewable and Sustainable Energy Reviews 133, no. : 110295.

Journal article
Published: 10 November 2016 in International Journal of Computational Intelligence Systems
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ACS Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Kun-Cheng Chen. Novel Approach to Tourism Analysis with Multiple Outcome Capability Using Rough Set Theory. International Journal of Computational Intelligence Systems 2016, 9, 1118 -1132.

AMA Style

Chun-Che Huang, Tzu-Liang (Bill) Tseng, Kun-Cheng Chen. Novel Approach to Tourism Analysis with Multiple Outcome Capability Using Rough Set Theory. International Journal of Computational Intelligence Systems. 2016; 9 (6):1118-1132.

Chicago/Turabian Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Kun-Cheng Chen. 2016. "Novel Approach to Tourism Analysis with Multiple Outcome Capability Using Rough Set Theory." International Journal of Computational Intelligence Systems 9, no. 6: 1118-1132.

Conference paper
Published: 28 June 2016 in Computer Vision
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“Energy” has been one element of the development of human civilization, also a power for national industry, construction and economic development. The green energy has become the cornerstone in sustainable development to secure such energy supply but may accommodate opinions from controversial perspectives when this subject is discussed. This study develops an interactive big data system, which aims at aggregating data from Facebook, PTT, news, and provides an interactive interface for energy domain experts. The “interaction” characterizes the seamless integration between users and the system to construct the controversial issue sets of energy, which could be identified and established autonomously in this study. The approach using tags of the link in two controversial issues can help end-users effectively query on demand. The energy relevant issues can be fully aware and provided to the decision makers from the positive and negative viewpoints.

ACS Style

Chun-Che Huang; Yu-Jie Fang; Shian-Hua Lin; Wen-Yau Liang; Shu-Rong Wu. Development of Issue Sets from Social Big Data: A Case Study of Green Energy and Low-Carbon. Computer Vision 2016, 139 -153.

AMA Style

Chun-Che Huang, Yu-Jie Fang, Shian-Hua Lin, Wen-Yau Liang, Shu-Rong Wu. Development of Issue Sets from Social Big Data: A Case Study of Green Energy and Low-Carbon. Computer Vision. 2016; ():139-153.

Chicago/Turabian Style

Chun-Che Huang; Yu-Jie Fang; Shian-Hua Lin; Wen-Yau Liang; Shu-Rong Wu. 2016. "Development of Issue Sets from Social Big Data: A Case Study of Green Energy and Low-Carbon." Computer Vision , no. : 139-153.

Journal article
Published: 01 April 2016 in Computer Methods and Programs in Biomedicine
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This paper presents a new heuristic algorithm for reduct selection based on credible index in the rough set theory (RST) applications. This algorithm is efficient and effective in selecting the decision rules particularly the problem to be solved in a large scale. This algorithm is capable to derive the rules with multi-outcomes and identify the most significant features simultaneously, which is unique and useful in solving predictive medical problems. The end results of the proposed approach are a set of decision rules that illustrates the causes for solitary pulmonary nodule and results of the long term treatment.

ACS Style

Tzu-Liang (Bill) Tseng; Chun-Che Huang; Kym Fraser; Hsien-Wei Ting. Rough set based rule induction in decision making using credible classification and preference from medical application perspective. Computer Methods and Programs in Biomedicine 2016, 127, 273 -289.

AMA Style

Tzu-Liang (Bill) Tseng, Chun-Che Huang, Kym Fraser, Hsien-Wei Ting. Rough set based rule induction in decision making using credible classification and preference from medical application perspective. Computer Methods and Programs in Biomedicine. 2016; 127 ():273-289.

Chicago/Turabian Style

Tzu-Liang (Bill) Tseng; Chun-Che Huang; Kym Fraser; Hsien-Wei Ting. 2016. "Rough set based rule induction in decision making using credible classification and preference from medical application perspective." Computer Methods and Programs in Biomedicine 127, no. : 273-289.

Journal article
Published: 01 January 2016 in Computers & Industrial Engineering
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In service industry application, there is vague and qualitative information required to be processed properly, for example, to identify customer preferences in order to provide adequate services. From literature, Rough Set Theory (RST) has been indicated to be one of promising approaches to cope with vagueness in a large scale database. Basically, the rough set approach integrates learning-from-example techniques, extracts rules from a data set of interest, and discovers data regularities. Most of the existing RS based approaches are able to implement rule induction but it is very time consuming from computation perspective particularly from a large database. To date, there is a demand to generate and analyze business decision rules based on dynamical data sets and conclude such rules on the daily basis in the service industry. Therefore, in this study, an Incremental Weight Incorporated Rule Identification (IWIRI) algorithm is proposed to fulfill such demand. The proposed approach is proficient to efficiently process in-coming data (objetcs) and generate updated decision rules without re-computation efforts in the database. Identification of features based on the customer’s preference and implementation of the proposed algorithm are summarized in the case study. This paper forms the basis for solving many other similar problems that occur in service industries.

ACS Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Chia-Ying Tang. Feature extraction using rough set theory in service sector application from incremental perspective. Computers & Industrial Engineering 2016, 91, 30 -41.

AMA Style

Chun-Che Huang, Tzu-Liang (Bill) Tseng, Chia-Ying Tang. Feature extraction using rough set theory in service sector application from incremental perspective. Computers & Industrial Engineering. 2016; 91 ():30-41.

Chicago/Turabian Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Chia-Ying Tang. 2016. "Feature extraction using rough set theory in service sector application from incremental perspective." Computers & Industrial Engineering 91, no. : 30-41.

Journal article
Published: 01 December 2015 in Applied Soft Computing
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Develop a solution approach to resolve the hierarchical rough set problem.Explore the most specific decision attribute level by level in the level-search procedure.Apply the proposed approach to the case in the transportation industry to select green fleets.This corporation reduces pollution emissions over the long-term by choosing the green vehicles in a desired ratio. Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.

ACS Style

Shian-Hua Lin; Chun-Che Huang; Zhi-Xing Che. Rule induction for hierarchical attributes using a rough set for the selection of a green fleet. Applied Soft Computing 2015, 37, 456 -466.

AMA Style

Shian-Hua Lin, Chun-Che Huang, Zhi-Xing Che. Rule induction for hierarchical attributes using a rough set for the selection of a green fleet. Applied Soft Computing. 2015; 37 ():456-466.

Chicago/Turabian Style

Shian-Hua Lin; Chun-Che Huang; Zhi-Xing Che. 2015. "Rule induction for hierarchical attributes using a rough set for the selection of a green fleet." Applied Soft Computing 37, no. : 456-466.

Book chapter
Published: 01 January 2015 in Advances in Intelligent Systems and Computing
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Providing sustainable service and energy has been becoming a trend due to environmental concerns. One of the academic challenges in sustainable service and energy is identified: In the complex service sector, those data (e.g., from questionnaires) may be complicated, qualitative and in large scale. Numerous attributes which are non-regular in nature and have the impact on service performance are involved. One of the promised solution approaches is the Rough Set (RS) based approach that can deal with qualitative information and provide an individual object model based approach. However, traditional RS approaches have a few disadvantages: (i) The decision attribute in one level only that can reflects the concept hierarchy, (ii) using two stages to generate reducts and induct decision rules. This paper, an extended RS based rule induction approach is proposed while decision tables are not in traditional format. This study contributes development of the solution models to sustainable service and energy.

ACS Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Yu-Sheng Liu; Jun-Wei Chu; Po-An Chen. Agile Rough Set Based Rule Induction to Sustainable Service and Energy Provision. Advances in Intelligent Systems and Computing 2015, 330, 761 -764.

AMA Style

Chun-Che Huang, Tzu-Liang (Bill) Tseng, Yu-Sheng Liu, Jun-Wei Chu, Po-An Chen. Agile Rough Set Based Rule Induction to Sustainable Service and Energy Provision. Advances in Intelligent Systems and Computing. 2015; 330 ():761-764.

Chicago/Turabian Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Yu-Sheng Liu; Jun-Wei Chu; Po-An Chen. 2015. "Agile Rough Set Based Rule Induction to Sustainable Service and Energy Provision." Advances in Intelligent Systems and Computing 330, no. : 761-764.

Journal article
Published: 12 April 2013 in Annals of Operations Research
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Rough set theory is a new data mining approach to manage vagueness. It is capable to discover important facts hidden in the data. Literature indicate the current rough set based approaches can’t guarantee that classification of a decision table is credible and it is not able to generate robust decision rules when new attributes are incrementally added in. In this study, an incremental attribute oriented rule-extraction algorithm is proposed to solve this deficiency commonly observed in the literature related to decision rule induction. The proposed approach considers incremental attributes based on the alternative rule extraction algorithm (AREA), which was presented for discovering preference-based rules according to the reducts with the maximum of strength index (SI), specifically the case that the desired reducts are not necessarily unique since several reducts could include the same value of SI. Using the AREA, an alternative rule can be defined as the rule which holds identical preference to the original decision rule and may be more attractive to a decision-maker than the original one. Through implementing the proposed approach, it can be effectively operating with new attributes to be added in the database/information systems. It is not required to re-compute the updated data set similar to the first step at the initial stage. The proposed algorithm also excludes these repetitive rules during the solution search stage since most of the rule induction approaches generate the repetitive rules. The proposed approach is capable to efficiently and effectively generate the complete, robust and non-repetitive decision rules. The rules derived from the data set provide an indication of how to effectively study this problem in further investigations.

ACS Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Fuhua Jiang; Yu-Neng Fan; Chih-Hua Hsu. Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes. Annals of Operations Research 2013, 216, 163 -189.

AMA Style

Chun-Che Huang, Tzu-Liang (Bill) Tseng, Fuhua Jiang, Yu-Neng Fan, Chih-Hua Hsu. Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes. Annals of Operations Research. 2013; 216 (1):163-189.

Chicago/Turabian Style

Chun-Che Huang; Tzu-Liang (Bill) Tseng; Fuhua Jiang; Yu-Neng Fan; Chih-Hua Hsu. 2013. "Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes." Annals of Operations Research 216, no. 1: 163-189.

Journal article
Published: 01 January 2013 in Decision Support Systems
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ACS Style

Chun-Che Huang; Yu-Neng Fan; Ching-Chin Chern; Pei-Hua Yen. Measurement of analytical knowledge-based corporate memory and its application. Decision Support Systems 2013, 54, 846 -857.

AMA Style

Chun-Che Huang, Yu-Neng Fan, Ching-Chin Chern, Pei-Hua Yen. Measurement of analytical knowledge-based corporate memory and its application. Decision Support Systems. 2013; 54 (2):846-857.

Chicago/Turabian Style

Chun-Che Huang; Yu-Neng Fan; Ching-Chin Chern; Pei-Hua Yen. 2013. "Measurement of analytical knowledge-based corporate memory and its application." Decision Support Systems 54, no. 2: 846-857.

Proceedings article
Published: 01 January 2013 in Special Session on Optimization and Sustainability
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ACS Style

Chun-Che Huang; Shian-Hua Lin; Zhi-Xing Chen; You-Ping Wang. A Rule Induction with Hierarchical Decision Attributes. Special Session on Optimization and Sustainability 2013, 95 -102.

AMA Style

Chun-Che Huang, Shian-Hua Lin, Zhi-Xing Chen, You-Ping Wang. A Rule Induction with Hierarchical Decision Attributes. Special Session on Optimization and Sustainability. 2013; ():95-102.

Chicago/Turabian Style

Chun-Che Huang; Shian-Hua Lin; Zhi-Xing Chen; You-Ping Wang. 2013. "A Rule Induction with Hierarchical Decision Attributes." Special Session on Optimization and Sustainability , no. : 95-102.

Journal article
Published: 30 September 2012 in Computer Standards & Interfaces
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Increasing demand for sophisticated software capable to collaborate, control, and organize all distributed activities has encouraged researchers in various disciplines to utilize and implement Intelligent Agent (IA). This paper develops a methodology to appraise performance of the IA and demonstrate the use in the B2C e-commerce negotiation process. An experiment was conducted to acquire empirical data and a survey was implemented to confirm advantage of the use of the IA. The computational results indicate that the proposed approach successfully evaluates IA performance and significantly distinguishes groups of using (vs. not using) the negotiation mechanism in B2C e-commerce.

ACS Style

Wen-Yau Liang; Chun-Che Huang; Tzu-Liang (Bill) Tseng; Yin-Chen Lin; Juotzu Tseng. The evaluation of intelligent agent performance — An example of B2C e-commerce negotiation. Computer Standards & Interfaces 2012, 34, 439 -446.

AMA Style

Wen-Yau Liang, Chun-Che Huang, Tzu-Liang (Bill) Tseng, Yin-Chen Lin, Juotzu Tseng. The evaluation of intelligent agent performance — An example of B2C e-commerce negotiation. Computer Standards & Interfaces. 2012; 34 (5):439-446.

Chicago/Turabian Style

Wen-Yau Liang; Chun-Che Huang; Tzu-Liang (Bill) Tseng; Yin-Chen Lin; Juotzu Tseng. 2012. "The evaluation of intelligent agent performance — An example of B2C e-commerce negotiation." Computer Standards & Interfaces 34, no. 5: 439-446.

Journal article
Published: 07 October 2010 in Expert Systems with Applications
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In this paper, application of the rough set theory (RST) to feature selection in customer relationship management (CRM) is introduced. Compared to other methods, the RST approach has the advantage of combining both qualitative and quantitative information in the decision analysis, which is extremely important for CRM. Automated decision support for CRM has been proposed in recent years. However, little work has been devoted to the development of computer-based systems to support CRM in rule induction. This paper presents a novel rough set based algorithm for automated decision support for CRM. Particularly, the approach is capable to handle real numbers instead of integer numbers through introduction of converted numbers involving tolerances. Being unique and useful in solving CRM problems, an alternative rule extraction algorithm (AREA) is presented for discovering preference-based rules according to the reducts which contain the maximum of strength index (SI) in the same case, where the data with tolerance. The empirical data set associated with CRM has proven the validity and reliability of these approaches. This research thus contributes to developing and validating a useful approach to automated decision support for CRM. This paper forms the basis for solving many other similar problems that occur in the service industry.

ACS Style

Tzu-Liang (Bill) Tseng; Chun-Che Huang; Yu-Neng Fan. Autonomous rule induction from data with tolerances in customer relationship management. Expert Systems with Applications 2010, 38, 4889 -4900.

AMA Style

Tzu-Liang (Bill) Tseng, Chun-Che Huang, Yu-Neng Fan. Autonomous rule induction from data with tolerances in customer relationship management. Expert Systems with Applications. 2010; 38 (5):4889-4900.

Chicago/Turabian Style

Tzu-Liang (Bill) Tseng; Chun-Che Huang; Yu-Neng Fan. 2010. "Autonomous rule induction from data with tolerances in customer relationship management." Expert Systems with Applications 38, no. 5: 4889-4900.

Journal article
Published: 30 April 2010 in Expert Systems with Applications
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Interoperability among multi-entities (companies) with heterogeneous knowledge sources becomes a research focus in the field of Supply chain management (SCM). Specifically, sharing knowledge among multiple entities in a supply chain is crucial. However, only a few studies have addressed the problem of interoperability and knowledge sharing in supply chains. Current technologies, such as EDI, RosettaNet or the current Web, are useful for sharing data/information, rather than knowledge. This paper proposes a solution for sharing knowledge with the semantic web. The solution involves (i) a semi-structured knowledge model to represent knowledge in not only an explicit and sharable, but also a meaningful format, (ii) an agent-based annotation process to resolve issues associated with the heterogeneity of knowledge documents, and (iii) an articulation mechanism to improve the effectiveness of interoperability between two heterogeneous ontologies. Based on the proposed solution, entities in a supply chain can represent, seek, and share knowledge effectively.

ACS Style

Chun-Che Huang; Shian-Hua Lin. Sharing knowledge in a supply chain using the semantic web. Expert Systems with Applications 2010, 37, 3145 -3161.

AMA Style

Chun-Che Huang, Shian-Hua Lin. Sharing knowledge in a supply chain using the semantic web. Expert Systems with Applications. 2010; 37 (4):3145-3161.

Chicago/Turabian Style

Chun-Che Huang; Shian-Hua Lin. 2010. "Sharing knowledge in a supply chain using the semantic web." Expert Systems with Applications 37, no. 4: 3145-3161.

Journal article
Published: 30 November 2009 in Expert Systems with Applications
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The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous rule-extraction algorithm is proposed to resolve there aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.

ACS Style

Yu-Neng Fan; Tzu-Liang (Bill) Tseng; Ching-Chin Chern; Chun-Che Huang. Rule induction based on an incremental rough set. Expert Systems with Applications 2009, 36, 11439 -11450.

AMA Style

Yu-Neng Fan, Tzu-Liang (Bill) Tseng, Ching-Chin Chern, Chun-Che Huang. Rule induction based on an incremental rough set. Expert Systems with Applications. 2009; 36 (9):11439-11450.

Chicago/Turabian Style

Yu-Neng Fan; Tzu-Liang (Bill) Tseng; Ching-Chin Chern; Chun-Che Huang. 2009. "Rule induction based on an incremental rough set." Expert Systems with Applications 36, no. 9: 11439-11450.

Journal article
Published: 30 April 2009 in Expert Systems with Applications
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Evolutionary computing (EC) techniques have been used traditionally used for solving challenging optimization problems. But the increase in data and information has reduced the performance capacity of the GA, but highlighted the cost of finding a solution by GA. In addition, the genetic algorithm employed in previous literature is modeled to solve one problem exactly. The GA needs to be redesigned, at a cost, for it to be applied to another problem. For these two reasons, this paper proposes a method for incorporating the GA and rough set theory. The superiority of the proposed GA in this paper lies in its ability to model problems and explore solutions generically. The advantages of the proposed solution approach include: (i) solving problems that can be decomposed into functional requirements, and (ii) improving the performance of the GA by reducing the domain range of the initial population and constrained crossover using rough set theory. The solution approach is exemplified by solving the problem of web services composition, where currently the general analysis and selection of services can be excessively complex and un-systemic. Based on our experimental results, this approach has shown great promise and operates effectively.

ACS Style

Wen-Yau Liang; Chun-Che Huang. The generic genetic algorithm incorporates with rough set theory – An application of the web services composition. Expert Systems with Applications 2009, 36, 5549 -5556.

AMA Style

Wen-Yau Liang, Chun-Che Huang. The generic genetic algorithm incorporates with rough set theory – An application of the web services composition. Expert Systems with Applications. 2009; 36 (3):5549-5556.

Chicago/Turabian Style

Wen-Yau Liang; Chun-Che Huang. 2009. "The generic genetic algorithm incorporates with rough set theory – An application of the web services composition." Expert Systems with Applications 36, no. 3: 5549-5556.