Learning cross-level certain and possible rules by rough sets

Hong, Tzung-Pei; Lin, Chun-E; Lin, Jiann-Horng; Wang, Shyue-Liang

doi:10.1016/j.eswa.2007.01.038

Cited by 37 publications

(10 citation statements)

References 10 publications

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“…No additional information or statistical assumption is needed (Goh & Law, 2003;Su & Hsu, 2006). The RST has been successfully applied in a variety of fields such as: business failure prediction (Ahn, Cho, & Kim, 2000;Beynon & Peel, 2001;Dimitras, Slowinski, Susmaga, & Zopounidis, 1999;Slowinski & Zopounidis, 1995), rough neural expert system (Yahia, Mahmod, Sulaiman, & Ahmad, 2000), maximally general fuzzy rules (Hong, Wang, Wang, & Chien, 2000), customer and product fragmentation (Changchien & Lu, 2001), rules from incomplete training examples (Hong, Tseng, & Wang, 2002), stock price mining (Wang, 2003), hierarchical decision rules from clinical databases (Tsumoto, 2003), case-based reasoning application (Huang & Tseng, 2004), travel pattern generation (Witlox & Tindemans, 2004), credit scoring (Ong, Huang, & Tzeng, 2005), bank credit ratings (Griffiths & Beynon, 2005), rule discovery from noisy data (Wang, 2005), group decision (Huang, Ong, & Tzeng, 2006), classification rules (Tsai, Cheng, & Chang, 2006), customer relationship management (Tseng & Huang, 2007), insurance market (Shyng, Wang, Tzeng, & Wu, 2007), drug utilization knowledge (Chou, Cheng, & Chang, 2007), supplier selection (Xia & Wu, 2007), location based services (Sikder & Gangopadhyay, 2007), neighborhood classifiers (Hu, Yu, & Xie, 2008) cross-level certain and possible rules (Hong, Lin, Lin, & Wang, 2008), feature selection (Chen, Tseng, & Hong, 2008), and so on. The basics of RST are explained below.…”

Section: Methodsmentioning

confidence: 99%

Exploring high-performers’ required competencies

Lee

2010

Expert Systems with Applications

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Section: Methodsmentioning

confidence: 99%

Exploring high-performers’ required competencies

Lee

2010

Expert Systems with Applications

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“…Definition 2.4 [14] hierarchical attribute values We find that hierarchical attributes existed generally in realistic environment through researches. The cardinal feature of hierarchical attributes is that the values of it can be presented by hierarchical trees.…”

Section: Definition 23mentioning

confidence: 99%

Optimize Algorithm of Decision Tree Based on Rough Sets Hierarchical Attributes

Zhang

Yue-jin

2008

2008 International Conference on Computational Intelligence and Security

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Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. And now it has been widely applied in constructing decision tree which has no hierarchical attributes inside. However, hierarchical attributes exist generally in realistic environment, which leads that decision making has max rules. Using max rules to build decision trees can optimize decision trees and has practical values as well. So, in order to deal with hierarchical attributes in decision tree, this paper try to design an optimize algorithm of decision tree based on rough sets hierarchical attributes (ARSHA), which works by combining the hierarchical attribute values and deleting the associated objects when max rules exist in decision table. So that the algorithm developed in this paper can abstract the simplest rule set that can cover all information for decision making. Finally, a real example is used to demonstrate its feasibility and efficiency.

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“…Decision attribute is "play". Build decision tree and abstract rules by ARSDT: 2,3,6,7,8,9,10,13,14,15,16,17,18,19,20…”

Section: Algorithm Arsdtmentioning

confidence: 99%

An Optimized Algorithm of Decision Tree Based on Rough Sets Model

Chen

Liu

2010

2010 International Conference on Electrical and Control Engineering

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An optimized decision tree algorithm based on rough sets model is proposed. Firstly the most popular decision tree algorithms, which are based rough sets model, usually partition examples too detailed to avoid the negative impact caused by a few special examples on decision tree because of the classification accuracy. The inhibitory factors are put forward in the forming process of the algorithm to cut branches for decision tree, avoiding redundant steps of cutting branches later. Secondly the condition attribute and decision attribute are matched in every division to avoid unnecessary calculation and improve the efficiency of the algorithm.

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Learning cross-level certain and possible rules by rough sets

Cited by 37 publications

References 10 publications

Exploring high-performers’ required competencies

Exploring high-performers’ required competencies

Optimize Algorithm of Decision Tree Based on Rough Sets Hierarchical Attributes

An Optimized Algorithm of Decision Tree Based on Rough Sets Model

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