Browsing by Author "Hsieh, Shu-Huei"

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Data mining for multi-domain decision-making based on rough set theory
(2006)
Shyng, Jhieh-Yu
;
Tzeng, Gwo-Hshiung
;
Hsieh, Shu-Huei
;
Wang, Fang-Kuo
;
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.
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.