Earticle

초록

영어

To produce a new Rule, many rule-based classifiers use one measure to select the best attribute-value pair. So, a lot of attribute-values have the same best values, and we cannot distinguish which attribute-value pair is the best. On the other hand, these classifiers usually combine the best attribute-value pairs to produce rules, whether they bias toward the same class label or not. To address issues, this paper proposes a new measure approach named deviation. Using the attribute-value deviation, it is easy to distinguish which attribute-value pairs bias toward the same class label. In this paper, we propose a multi-measure called effect measure to select the best attribute-value pair. It integrates the deviation and the entropy, and we also propose a new classification approach called CAEM which uses the effect measure to select the best attribute-value pairs. Experimental results show the method of multi-measure is necessary.

목차

Abstract
 1. Introduction
 2. The Effect Measure of Attribute-value
 3. Classification based on the Attribute-value Pair Effect Measure
  3.1. Group all Attribute-value Pairs by their Deviation
  3.2. Calculate all Attribute-value Pair’s Effects in the Group
  3.2. Producing Rules
  3.3 Classification using Rules
 4. Experiments
  4.1. Experiments of the Binary Class
  4.2. Experiments of the Multi-class
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Tianzhong He [ Department of Computer Science and Engineering, Minnan Normal University ]
• Zhongmei Zhou [ Department of Computer Science and Engineering, Minnan Normal University ]
• Zaixiang Huang [ Department of Computer Science and Engineering, Minnan Normal University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605