標題: Compactness rate as a rule selection index based on Rough Set Theory to improve data analysis for personal investment portfolios
作者: Shyng, Jhieh-Yu
Shieh, How-Ming
Tzeng, Gwo-Hshiung
科技管理研究所
Institute of Management of Technology
關鍵字: Rough Set Theory (RST);Compactness rate;Strength rate;Support;Investment portfolio
公開日期: 1-Jun-2011
摘要: 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.
URI: http://dx.doi.org/10.1016/j.asoc.2011.01.038
http://hdl.handle.net/11536/8820
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2011.01.038
期刊: APPLIED SOFT COMPUTING
Volume: 11
Issue: 4
起始頁: 3671
結束頁: 3679
Appears in Collections:Articles


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