Browsing by Author "Shyng, Jhieh-Yu"

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An integration method combining Rough Set Theory with formal concept analysis for personal investment portfolios
(2010-08-01)
Shyng, Jhieh-Yu
;
Shieh, How-Ming
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
The classical Rough Set Theory (RST) always generates too many rules, making it difficult for decision makers to choose a suitable rule. In this study, we use two processes (pre process and post process) to select suitable rules and to explore the relationship among attributes In pre process, we propose a pruning process to select suitable rules by setting up a threshold on the support object of decision rules, to thereby solve the problem of too many rules The post process used the formal concept analysis from these suitable rules to explore the attribute relationship and the most important factors affecting decision making for choosing behaviours of personal investment portfolios In this study, we explored the main concepts (characteristics) for the conservative portfolio the stable job, less than 4 working years, and the gender is male, the moderate portfolio. high school education, the monthly salary between NT$30,001 (US$1000) and NT$80,000 (US$2667), the gender is male, and the aggressive portfolio. the monthly salary between NT$30,001 (US$1000) and NT$80,000 (US$2667), less than 4 working years, and a stable job The study result successfully explored the most important factors affecting the personal investment portfolios and the suitable rules that can help decision makers. (C) 2010 Elsevier B.V All rights reserved.
Compactness rate as a rule selection index based on Rough Set Theory to improve data analysis for personal investment portfolios
(2011-06-01)
Shyng, Jhieh-Yu
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Shieh, How-Ming
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
This study proposes a selection index technique, namely a compactness rate based on Rough Set Theory (RST), for improving data analysis, eliminating data amount and reducing the number of decision rule. This study uses an empirical real-case involving a personal investment portfolio to demonstrate the proposed method. The presented case includes 75 rules generated by the RST. The rules are vague and fragmentary, making it very difficult to interpret the information. Many rules have the same strength and number of support objects and condition parts. These are creating a critical problem for decision making. The new method proposed in this study not only enables the selection of interesting rules, but it also reduces the data amount, and offers alternative strategies that can help decision-makers analyze data. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
Data mining for multi-domain decision-making based on rough set theory
(2006)
Shyng, Jhieh-Yu
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Tzeng, Gwo-Hshiung
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Hsieh, Shu-Huei
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Wang, Fang-Kuo
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科技管理研究所
In the modern world, it is difficult to discover knowledge from the same information with multi-purpose. Usually, it is only possible to consider one purpose at a time. If there have same information corresponding with multi-attribute decisions. The usual approach is separate the decision attributes by instinct or by expert knowledge in order to simplify the purpose decisions. This, however, becomes more difficult when there are many multi-attribute decisions. Our proposed method uses the reduct process of Rough Set Theory to separate multi-attribute decisions into multi-domains to simplify the analysis. There also exist relations among the domains. Generating decision tables by relation degree avoid the shortcomings of human decision-making and are better for decision qualification. An empirical study of multi-domain decision-making in the insurance market is used to illustrate the reduct process in our proposed method. The results demonstrate that the reduct process can separate multi-attribute decisions by attribute domains successfully. We take seven decision tables that were originally drawn up based on instinct or expert knowledge and simplify them into only three decision tables. This enhances the precision of the decision explanation. Decision tables generated according to domains are more scientific than those derived by traditional methods and more useful for data analysis.
Exploring the preference of customers between financial companies and agents based on TCA
(2012-03-01)
Fang, Sheng-Kai
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Shyng, Jhieh-Yu
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Lee, Wen-Shiung
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Tzeng, Gwo-Hshiung
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科技管理研究所
Based on transaction cost analysis (TCA), this research explores the customers' loyalty to either the financial companies or the company financial agents with whom they have established relationship. In the past, consumers were divided into those who rely on agents and those who do not. In this study, we use two processes (pre-process and post-process) to select suitable rules, and to explore into the relationship among attributes. In the pre-process, we utilized factor analysis (FA) to choose the variable and rough set theory (RST) that found decision table to construct the decision rules, and approach to data mining and knowledge discovery based on information flow distribution in a flow graph. The post-process applies the formal concept analysis (FCA) from these suitable rules to explore the attribute relationship and the most important factors affecting the preference of customers for deciding whether to choose companies or agents. The degree of the customers' dependence on agents was affected by the TCA, customer satisfaction and loyalty. The principal findings were that the different degrees of dependence of customers have various characteristics. The RST and FCA were two complementary mathematical tools for data analysis. Following an empirical analysis, we use two hit testes that incorporate 30 and 36 validated sample object into the decision rule. The hitting rate of two testes, were reached 90%. The results of the empirical study indicate that the generated decision rules can cover most new objects. Consequently, we believe that the result can be fully applied in financial research. (C) 2011 Published by Elsevier B.V.
Using FSBT technique with Rough Set Theory for personal investment portfolio analysis
(2010-03-01)
Shyng, Jhieh-Yu
;
Shieh, How-Ming
;
Tzeng, Gwo-Hshiung
;
Hsieh, Shu-Huei
;
科技管理研究所
This study proposes a novel Forward Search and Backward Trace (FSBT) technique based on Rough Set Theory to improve data analysis and extend the scope of observations made from sample data to solve personal investment portfolio problems. Rough Set Theory mathematically classifies data into class sets. The class set with the most objects may generate one decision rule. The rules generated from RST are rough and fragmented, that are very difficult to interpret the information. An empirical case is used to generate more than 85 rules by the RST method in comparison with FSBT method which only generated 14 rules. This result can show our proposed method is better than traditional RST method based on class sets that contain the most objects. Much of human knowledge is described in natural language. It is a very important thing to convert information from computer databases into normal human language. Sample data taken from features with the same backgrounds are used to compile different portfolios that investment companies and investment advisors can employ to satisfy the investor' needs. The method not only can provide decision-making rules, but also can offer alternative strategies for better data analysis. We believe that the FSBT technique can be fully applied in research on investment marketing. (C) 2009 Elsevier B.V. All rights reserved.