An approach for adaptive associative classification

Authors:
Xiaofeng Wang;Kun Yue;WenJia Niu;Zhongzhi Shi
Affiliations:
The Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate University of Chinese Academy of Sciences, Beijing, C ...;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China;The Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

As a branch of classification, associative classification combines the basic ideas of association rule mining and general classification. Previous studies show that associative classification can achieve a higher classification accuracy comparing with traditional classification methods, such as C4.5. It is known that new frequent patterns may emerge from the classified resources during classification, and these newly emerging frequent patterns can be used to build new classification rules. However, this dynamic characteristics in associative classification has not been well reflected in traditional methods. In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ co-training to refine the discovered emerging frequent patterns for classification rule extension and utilize the maximum entropy model for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.