원문정보
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
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