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Banks attempt to invest in emerging financial technology (FinTech), such as blockchain, to enhance competitiveness. There is a great deal of literature on the technical and legal aspects of blockchain. However, there is little specific guidance on how banks can apply a holistic model to evaluate the blockchain-based business. This study proposes a hybrid decision model with confidence-weighted fuzzy assessments to address this valuable research topic. Supported by a group of seasoned experts, five major blockchain-based business models are evaluated for a domestic bank in Taiwan. The key findings contribute to understanding the importance of the involved factors and identifying the ideal business strategy for the bank. The result suggests that the most crucial dimension is policies and regulations, not the technical capability of banks.
Nien-Ping Chen; Kao-Yi Shen; Chiung-Ju Liang. Hybrid Decision Model for Evaluating Blockchain Business Strategy: A Bank’s Perspective. Sustainability 2021, 13, 5809 .
AMA StyleNien-Ping Chen, Kao-Yi Shen, Chiung-Ju Liang. Hybrid Decision Model for Evaluating Blockchain Business Strategy: A Bank’s Perspective. Sustainability. 2021; 13 (11):5809.
Chicago/Turabian StyleNien-Ping Chen; Kao-Yi Shen; Chiung-Ju Liang. 2021. "Hybrid Decision Model for Evaluating Blockchain Business Strategy: A Bank’s Perspective." Sustainability 13, no. 11: 5809.
Open banking (OB) is an emerging business field in the financial sector, which relies on intensive collaboration between banks and non-banking service providers. However, how to evaluate OB business partners from multiple perspectives for banks is underexplored. Therefore, this study proposed a hybrid decision model with supports from seasoned domain experts. This study also adopts a domestic bank from Taiwan and four non-banking service providers to illustrate the hybrid approach with the confidence-weighted fuzzy assessment technique. The proposed model might be the first attempt to explore the OB adoption strategy by the novel approach. However, its limitations are the presumed independent relationship among the factors of this hybrid model. Additionally, the results hinge upon domain experts’ knowledge. In practice, the research findings identify the relative importance of banks’ crucial factors to select OB strategic partners, which provide managerial insights and valuable guidance for the banking sector.
Alexander Daiy; Kao-Yi Shen; Jim-Yuh Huang; Tom Lin. A Hybrid MCDM Model for Evaluating Open Banking Business Partners. Mathematics 2021, 9, 587 .
AMA StyleAlexander Daiy, Kao-Yi Shen, Jim-Yuh Huang, Tom Lin. A Hybrid MCDM Model for Evaluating Open Banking Business Partners. Mathematics. 2021; 9 (6):587.
Chicago/Turabian StyleAlexander Daiy; Kao-Yi Shen; Jim-Yuh Huang; Tom Lin. 2021. "A Hybrid MCDM Model for Evaluating Open Banking Business Partners." Mathematics 9, no. 6: 587.
From the clinical viewpoint, the statistical approach is still the cornerstone for exploring many diseases. This study was conducted to explore the risk factors related to acute kidney injury (AKI) for elderly patients using the multiple criteria decision-making (MCDM) approach. Ten nephrologists from a teaching hospital in Taipei took part in forming the AKI risk assessment model. The key findings are: (1) Comorbidity and Laboratory Values would influence Comprehensive Geriatric Assessment; (2) Frailty is the highest influential AKI risk factor for elderly patients; and (3) Elderly patients could enhance their daily activities and nutrition to improve frailty and lower AKI risk. Furthermore, we illustrate how to apply MCDM methods to retrieve clinical experience from seasoned doctors, which may serve as a knowledge-based system to support clinical prognoses. In conclusion, this study has shed light on integrating multiple research approaches to assist medical decision-making in clinical practice.
Kao-Yi Shen; Yen-Ching Chuang; Tao-Hsin Tung. Clinical Knowledge Supported Acute Kidney Injury (AKI) Risk Assessment Model for Elderly Patients. International Journal of Environmental Research and Public Health 2021, 18, 1607 .
AMA StyleKao-Yi Shen, Yen-Ching Chuang, Tao-Hsin Tung. Clinical Knowledge Supported Acute Kidney Injury (AKI) Risk Assessment Model for Elderly Patients. International Journal of Environmental Research and Public Health. 2021; 18 (4):1607.
Chicago/Turabian StyleKao-Yi Shen; Yen-Ching Chuang; Tao-Hsin Tung. 2021. "Clinical Knowledge Supported Acute Kidney Injury (AKI) Risk Assessment Model for Elderly Patients." International Journal of Environmental Research and Public Health 18, no. 4: 1607.
While the importance of Corporate Sociable Responsibility (CSR) has been widely acknowledged, research on how to guide a company in evaluating and improving its CSR performance is relatively under-explored. This paper adopts the predominant framework from the United Nations (UN) and proposes a refined CSR model by using a hybrid multiple criteria decision-making (MCDM) approach. The proposed approach is expected to mitigate the potential information asymmetry issue that might deteriorate the CSR performance of a company. To illustrate the hybrid approach, this study analyzes the CSR performance of four publicly listed information technology (IT) manufacturing companies with the participation of senior domain experts, by using the proposed approach. The CSR performance ranking results are consistent by using various experiments, which is similar to the annual CSR contest held by a prominent organization from Taiwan in 2019. In addition, we illustrate how to apply this refined model to gain managerial insights and pursue sustainable CSR improvement with a priority.
Ya-Lan Wang; Kao-Yi Shen; Jim-Yuh Huang; Pin Luarn. Use of a Refined Corporate Social Responsibility Model to Mitigate Information Asymmetry and Evaluate Performance. Symmetry 2020, 12, 1349 .
AMA StyleYa-Lan Wang, Kao-Yi Shen, Jim-Yuh Huang, Pin Luarn. Use of a Refined Corporate Social Responsibility Model to Mitigate Information Asymmetry and Evaluate Performance. Symmetry. 2020; 12 (8):1349.
Chicago/Turabian StyleYa-Lan Wang; Kao-Yi Shen; Jim-Yuh Huang; Pin Luarn. 2020. "Use of a Refined Corporate Social Responsibility Model to Mitigate Information Asymmetry and Evaluate Performance." Symmetry 12, no. 8: 1349.
Zhiwen Jian; Hiroshi Sakai; Takuya Ohwa; Kao-Yi Shen; Michinori Nakata. An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions. 2020, 12179, 95 -110.
AMA StyleZhiwen Jian, Hiroshi Sakai, Takuya Ohwa, Kao-Yi Shen, Michinori Nakata. An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions. . 2020; 12179 ():95-110.
Chicago/Turabian StyleZhiwen Jian; Hiroshi Sakai; Takuya Ohwa; Kao-Yi Shen; Michinori Nakata. 2020. "An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions." 12179, no. : 95-110.
This study proposes a novel multiple rule-base decision-making (MRDM) model to transform the current bipolar model into a multi-graded one based on the theoretical foundation of rough set approximations. In the existing bipolar model, the decision class (DC) comprises only three classes: positive, others, and negative ones, and the induced positive or negative rules by the dominance-based rough set approach (DRSA) or variable-consistency dominance-based rough set approach (VC-DRSA) are constrained by the dominance relationship. In certain scenarios or applications, the decision attribute of a bipolar model might need to be transformed into multi-graded DCs to meet practices; examples are the commonly observed Likert 5-point scale questionnaire adopted in a marketing survey. In other words, by eliciting a decision maker’s (DM’s) preferential judgements on the preferred degree of each DC, the newly proposed model may be more flexible to reflect the DM’s preferences or knowledge on modeling an application in a more delicate manner. To reach this goal, the present study proposes a novel MRDM model with multi-graded preferential degree of each DC. Furthermore, the performance of each alternative’s score on each rule can be assessed by the crisp (i.e., binary) or fuzzy set technique (FST) and aggregated by a linear or nonlinear operator. This study provides an exemplary case by evaluating the performance of a group of financial holding companies in Taiwan by using the binary assessment and the simple additive weight (SAW) aggregator. The obtained ranking by evaluating their financial data in 2016 is consistent with their actual financial performance in 2017, which suggests the validity of the proposed model.
Kao-Yi Shen; Hiroshi Sakai; Gwo-Hshiung Tzeng. Multi-graded Hybrid MRDM Model for Assisting Financial Performance Evaluation Decisions: A Preliminary Work. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 439 -453.
AMA StyleKao-Yi Shen, Hiroshi Sakai, Gwo-Hshiung Tzeng. Multi-graded Hybrid MRDM Model for Assisting Financial Performance Evaluation Decisions: A Preliminary Work. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():439-453.
Chicago/Turabian StyleKao-Yi Shen; Hiroshi Sakai; Gwo-Hshiung Tzeng. 2019. "Multi-graded Hybrid MRDM Model for Assisting Financial Performance Evaluation Decisions: A Preliminary Work." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 439-453.
For each implication \(\tau : Condition\_part\Rightarrow Decision\_part\) defined in table data sets, we see \(\tau \) is a rule if \(\tau \) satisfies appropriate constraints, i.e., \(support(\tau )\ge \alpha \) and \(accuracy(\tau )\ge \beta \) for two threshold values \(\alpha \) and \(\beta \) (\(0<\alpha , \beta \le 1\)). If \(\tau \) is a rule for relatively high \(\alpha \), we say \(\tau \) is supported by major instances. On the other hand, if \(\tau \) is a rule for lower \(\alpha \), we say \(\tau \) is supported by minor instances. This paper focuses on rules supported by minor instances, and clarifies some problems. Then, the NIS-Apriori algorithm, which was proposed for handling rules supported by major instances from tables with information incompleteness, is extended to the NIS-Apriori algorithm with a target descriptor. The effectiveness of the new algorithm is examined by some experiments.
Hiroshi Sakai; Kao-Yi Shen; Michinori Nakata. NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances. Computer Vision 2019, 247 -259.
AMA StyleHiroshi Sakai, Kao-Yi Shen, Michinori Nakata. NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances. Computer Vision. 2019; ():247-259.
Chicago/Turabian StyleHiroshi Sakai; Kao-Yi Shen; Michinori Nakata. 2019. "NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances." Computer Vision , no. : 247-259.
While the importance of corporate governance has been broadly acknowledged in global financial markets and academic research, how to devise a practical evaluation system is relatively unexplored. This paper attempts to refine the Corporate Governance Evaluation System (CGES), constructed by the Taiwan Stock Exchange (TWSE) since 2014. The current CGES has several debatable issues in its complicated design (e.g., it comprises over 80 indicators in different types). To resolve those issues, this study invited ten senior domain experts (including several CEOs of financial holding companies) to retrieve 13 essential criteria from the CGES in four dimensions. Additionally, this study integrates several multiple criteria decision-making (MCDM) methods (i.e., decision-making trial and evaluation laboratory (DEMATEL), modified VIKOR, DEMATEL-based analytical network process (DANP)) and the fuzzy evaluation technique to rank the exemplary companies. The final ranking is consistent with the one released from the CGES in 2017. This study conducted additional experiments to ensure the robustness of the findings. The newly devised model not only assists the ranking decisions but also supports a company in discussing the plausible action plans to strengthen corporate governance based on the analytics. These findings enrich the understanding of corporate governance and contribute to gaining business sustainability for financial holding companies.
Jim-Yuh Huang; Kao-Yi Shen; Joseph C.P. Shieh; Gwo-Hshiung Tzeng. Strengthen Financial Holding Companies’ Business Sustainability by Using a Hybrid Corporate Governance Evaluation Model. Sustainability 2019, 11, 582 .
AMA StyleJim-Yuh Huang, Kao-Yi Shen, Joseph C.P. Shieh, Gwo-Hshiung Tzeng. Strengthen Financial Holding Companies’ Business Sustainability by Using a Hybrid Corporate Governance Evaluation Model. Sustainability. 2019; 11 (3):582.
Chicago/Turabian StyleJim-Yuh Huang; Kao-Yi Shen; Joseph C.P. Shieh; Gwo-Hshiung Tzeng. 2019. "Strengthen Financial Holding Companies’ Business Sustainability by Using a Hybrid Corporate Governance Evaluation Model." Sustainability 11, no. 3: 582.
In the recent years, various statistical and computational intelligence or machine learning techniques have contributed to the progress of automation or semiautomation for measuring consumer credit scoring in the banking sector. However, most of the Taiwanese commercial banks still rely on seasoned staffs’ judgments on making the final approvals or rejections. To enhance the understanding and transparency of a decision support system (or model) that can assist bank staffs on making their consumer credit loan decisions—while uncertainty exist—is of high business value. One of the promising approaches is multiple rule-based decision-making (MRDM), a subfield of the hybrid multiple criteria decision-making that leverages the advantages of machine learning, soft computing, and decision methods (or techniques). The MRDM approach reveals comprehensible logics (rules or patterns) that can be justified and compared with the existing knowledge of veterans to reinforce the confidence of their judgments. Therefore, in the present study, we propose and compare two MRDM approaches in assisting decision makers on the consumer credit loan evaluations. A set of historical data from a commercial bank in Taiwan is analyzed for illustrating the plausible pros and cons of the two approaches with discussions.
Kao-Yi Shen; Hioshi Sakai; Gwo-Hshiung Tzeng. Comparing Two Novel Hybrid MRDM Approaches to Consumer Credit Scoring Under Uncertainty and Fuzzy Judgments. International Journal of Fuzzy Systems 2018, 21, 194 -212.
AMA StyleKao-Yi Shen, Hioshi Sakai, Gwo-Hshiung Tzeng. Comparing Two Novel Hybrid MRDM Approaches to Consumer Credit Scoring Under Uncertainty and Fuzzy Judgments. International Journal of Fuzzy Systems. 2018; 21 (1):194-212.
Chicago/Turabian StyleKao-Yi Shen; Hioshi Sakai; Gwo-Hshiung Tzeng. 2018. "Comparing Two Novel Hybrid MRDM Approaches to Consumer Credit Scoring Under Uncertainty and Fuzzy Judgments." International Journal of Fuzzy Systems 21, no. 1: 194-212.
This paper focuses on two Apriori-based rule generators. The first is the rule generator in Prolog and C, and the second is the one in SQL. They are namedApriori in PrologandApriori in SQL, respectively. Each rule generator is based on the Apriori algorithm. However, each rule generator has its own properties. Apriori in Prolog employs the equivalence classes defined by table data sets and follows the framework of rough sets. On the other hand, Apriori in SQL employs a search for rule generation and does not make use of equivalence classes. This paper clarifies the properties of these two rule generators and considers effective applications of each to existing data sets.
Hiroshi Sakai; Kao-Yi Shen; Michinori Nakata. On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL. Journal of Advanced Computational Intelligence and Intelligent Informatics 2018, 22, 394 -403.
AMA StyleHiroshi Sakai, Kao-Yi Shen, Michinori Nakata. On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL. Journal of Advanced Computational Intelligence and Intelligent Informatics. 2018; 22 (3):394-403.
Chicago/Turabian StyleHiroshi Sakai; Kao-Yi Shen; Michinori Nakata. 2018. "On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 3: 394-403.
With the surging complexity of real-world problems in important domains such as sustainability, there is a need to leverage advanced modern computational methods or intelligent techniques to support decisions or policy-making. In this Special Issue, 15 selected and formally peer-reviewed papers contribute their novelty and findings, by applying various advanced decision methods or computational techniques to resolve different sustainability problems. Despite the innovations of the proposed models, most of the selected papers involve domain expert’s opinions and knowledge with in-depth discussions. These case studies enrich the practical contributions of this Special Issue.
Kao-Yi Shen; Gwo-Hshiung Tzeng. Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications. Sustainability 2018, 10, 1600 .
AMA StyleKao-Yi Shen, Gwo-Hshiung Tzeng. Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications. Sustainability. 2018; 10 (5):1600.
Chicago/Turabian StyleKao-Yi Shen; Gwo-Hshiung Tzeng. 2018. "Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications." Sustainability 10, no. 5: 1600.
Kao-Yi Shen; Edmundas Kazimieras Zavadskas; Gwo-Hshiung Tzeng. Updated discussions on ‘Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues’. Economic Research-Ekonomska Istraživanja 2018, 31, 1437 -1452.
AMA StyleKao-Yi Shen, Edmundas Kazimieras Zavadskas, Gwo-Hshiung Tzeng. Updated discussions on ‘Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues’. Economic Research-Ekonomska Istraživanja. 2018; 31 (1):1437-1452.
Chicago/Turabian StyleKao-Yi Shen; Edmundas Kazimieras Zavadskas; Gwo-Hshiung Tzeng. 2018. "Updated discussions on ‘Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues’." Economic Research-Ekonomska Istraživanja 31, no. 1: 1437-1452.
The importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrates a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, is analyzed as a case study. More than 30 variables are considered, and the adopted soft computing technique retrieves 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.
Kao-Yi Shen; Min-Ren Yan; Gwo-Hshiung Tzeng. Exploring R&D Influences on Financial Performance for Business Sustainability Considering Dual Profitability Objectives. Sustainability 2017, 9, 1964 .
AMA StyleKao-Yi Shen, Min-Ren Yan, Gwo-Hshiung Tzeng. Exploring R&D Influences on Financial Performance for Business Sustainability Considering Dual Profitability Objectives. Sustainability. 2017; 9 (11):1964.
Chicago/Turabian StyleKao-Yi Shen; Min-Ren Yan; Gwo-Hshiung Tzeng. 2017. "Exploring R&D Influences on Financial Performance for Business Sustainability Considering Dual Profitability Objectives." Sustainability 9, no. 11: 1964.
Gwo-Hshiung Tzeng; Kao-Yi Shen. New Concepts and Trends of Hybrid Multiple Criteria Decision Making. New Concepts and Trends of Hybrid Multiple Criteria Decision Making 2017, 1 .
AMA StyleGwo-Hshiung Tzeng, Kao-Yi Shen. New Concepts and Trends of Hybrid Multiple Criteria Decision Making. New Concepts and Trends of Hybrid Multiple Criteria Decision Making. 2017; ():1.
Chicago/Turabian StyleGwo-Hshiung Tzeng; Kao-Yi Shen. 2017. "New Concepts and Trends of Hybrid Multiple Criteria Decision Making." New Concepts and Trends of Hybrid Multiple Criteria Decision Making , no. : 1.
The modern business environment is full of uncertain and imprecise circumstances that require decision makers (DMs) to conduct informed and circumspect decisions. In this regard, rough set theory (RST) has been widely acknowledged as capable to resolve these complicated problems while relevant knowledge can be extracted—in the form of rules—for decision aids. By using those learned rules, an innovative bipolar decision model that comprises the positive (preferred) and negative (unwanted) rules, can be applied to rank alternatives based on their similarity to the positive and the dissimilarity to the negative ones. However, in some business cases (e.g., personal credit loan), applicants need to provide information (values) on all the attributes, requested by a bank. Sometimes, experienced evaluators (e.g., senior bank staff) might question the validity of some values (direct or indirect evidences) provided by an applicant. In such a case, evaluators may assign additional values to those attributes (regarded as non-deterministic ones) in a bipolar model, to examine the stability of a rule that is supported by questionable instances. How to select those rules with satisfactory stability would be an important issue to enhance the effectiveness of a bipolar decision model. As a result, the present study adopts the idea of stability factor, proposed by Sakai et al. [1], to enhance the effectiveness of a bipolar decision model, and a case of credit loan evaluation, with partially assumed values on several non-deterministic attributes, is illustrated with the discussions of potential application in practice.
Kao-Yi Shen; Hiroshi Sakai; Gwo-Hshiung Tzeng. Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 317 -328.
AMA StyleKao-Yi Shen, Hiroshi Sakai, Gwo-Hshiung Tzeng. Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():317-328.
Chicago/Turabian StyleKao-Yi Shen; Hiroshi Sakai; Gwo-Hshiung Tzeng. 2017. "Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 317-328.
The debate of “short-termism” has gained increasing interests from various fields, ranging from management to economics; it mainly concerns the decisions or actions taken by businesses that might yield short-term returns at the cost of long-term value or sustainability. Previous studies have highlighted this dilemma faced by managers, mainly from the pressure of capital markets or short-sighted shareholders who crave for immediate financial outcomes; intelligent decision aids that can compromise between the short- and long-term financial sustainability, based on a company’s policy, are highly needed. Therefore, the aim of this study is to develop a multiple-rule-based hybrid decision model to support management teams on prioritizing new R&D projects, considering the financial prospects in dual timeframes (i.e., short- and long-term) for sustainability. Furthermore, in the presence of business uncertainty and the limited knowledge of managers on new projects, the intuitionistic fuzzy technique is incorporated. A case of selecting new R&D projects for an IC design company is illustrated using the proposed approach, and the financial data from a group of public-listed IC stocks from Taiwan are inducted to form the decision model. The findings not only support the IC design company to select new projects but also provide business insights to facilitate the understandings of this controversial issue in managerial practice.
Kao-Yi Shen. Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions. Sustainability 2017, 9, 375 .
AMA StyleKao-Yi Shen. Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions. Sustainability. 2017; 9 (3):375.
Chicago/Turabian StyleKao-Yi Shen. 2017. "Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions." Sustainability 9, no. 3: 375.
Financial modeling for the life insurance industry involves two main difficulties: (1) Selecting the minimal and critical variables for modeling while considering the impreciseness and interrelationships among the numerous attributes and (2) measuring plausible synergy effects among variables and dimensions that might cause undesirable biases for an evaluation model. To overcome these difficulties, this paper proposes a two-stage hybrid approach: Rough financial knowledge is retrieved first, and then the obtained core attributes are measured and synthesized using fuzzy-integral-based decision methods. The main innovation of this study is the use of rough knowledge retrieval procedures and fuzzy measures for exploring the synergy effects on financial performance. This approach is expected to support insurers to systematically improve their financial performance. A group of life insurance companies in Taiwan was analyzed, and the findings support the existence of interrelated synergy effects among the core criteria. In addition, five companies were examined to illustrate financial performance improvement planning with this approach. This study bridges the gap between advanced soft computing techniques and pragmatic financial modeling in a dynamic business environment.
Kao-Yi Shen; Shu-Kung Hu; Gwo-Hshiung Tzeng. Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model. Information Sciences 2017, 375, 296 -313.
AMA StyleKao-Yi Shen, Shu-Kung Hu, Gwo-Hshiung Tzeng. Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model. Information Sciences. 2017; 375 ():296-313.
Chicago/Turabian StyleKao-Yi Shen; Shu-Kung Hu; Gwo-Hshiung Tzeng. 2017. "Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model." Information Sciences 375, no. : 296-313.
Kao-Yi Shen; Chinese Culture University; Gwo-Hshiung Tzeng; National Taipei University. ROUGH-RULES-BASED DECISION MODEL FOR MULTIPLE OBJECTIVES PORTFOLIO OPTIMIZATION. 2016, 1 .
AMA StyleKao-Yi Shen, Chinese Culture University, Gwo-Hshiung Tzeng, National Taipei University. ROUGH-RULES-BASED DECISION MODEL FOR MULTIPLE OBJECTIVES PORTFOLIO OPTIMIZATION. . 2016; ():1.
Chicago/Turabian StyleKao-Yi Shen; Chinese Culture University; Gwo-Hshiung Tzeng; National Taipei University. 2016. "ROUGH-RULES-BASED DECISION MODEL FOR MULTIPLE OBJECTIVES PORTFOLIO OPTIMIZATION." , no. : 1.
This study proposes a bipolar model for resolving multiple criteria decision-making (MCDM) problems, based on the integration of rough set theory and three-way decisions, for forming a hybrid bipolar model. It begins by dividing a decision space into three disjoint regions: negative, neutral, and positive states, in the next, a rough set theory based rule induction mechanism generates two sets of rules associated with the positive and the negative states respectively, termed as the positive and the negative rules. The two groups of rules are regarded the bipolar rough experience (knowledge) in the form of rules and granulized knowledge, and decision makers can then define a threshold to select the covered number of rules in the bipolar decision model. This novel approach not only supports decision makers to select or rank alternatives, but also identify rough knowledge for the addressed problem. As a result, this novel bipolar model transforms soft computing analytics into a comprehensible bipolar model for decision aids.
Kao-Yi Shen; Gwo-Hshiung Tzeng. A Novel Bipolar MCDM Model Using Rough Sets and Three-Way Decisions for Decision Aids. 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) 2016, 53 -58.
AMA StyleKao-Yi Shen, Gwo-Hshiung Tzeng. A Novel Bipolar MCDM Model Using Rough Sets and Three-Way Decisions for Decision Aids. 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS). 2016; ():53-58.
Chicago/Turabian StyleKao-Yi Shen; Gwo-Hshiung Tzeng. 2016. "A Novel Bipolar MCDM Model Using Rough Sets and Three-Way Decisions for Decision Aids." 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) , no. : 53-58.
Kao-Yi Shen; Gwo-Hshiung Tzeng. Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics. International Journal of Fuzzy Systems 2016, 18, 940 -955.
AMA StyleKao-Yi Shen, Gwo-Hshiung Tzeng. Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics. International Journal of Fuzzy Systems. 2016; 18 (6):940-955.
Chicago/Turabian StyleKao-Yi Shen; Gwo-Hshiung Tzeng. 2016. "Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics." International Journal of Fuzzy Systems 18, no. 6: 940-955.