Discriminatively Learning Selective Averaged One-Dependence Estimators Based on Cross-Entropy Method

Authors:
Qing Wang;Chuan-Hua Zhou;Bao-Hua Zhao
Affiliations:
School of Management Science and Engineering, Anhui University of Technology, Ma'anshan 243002, China;School of Management Science and Engineering, Anhui University of Technology, Ma'anshan 243002, China and Department of Computer Science and Engineering, University of Science and Technology, of C ...;Department of Computer Science and Engineering, University of Science and Technology, of China, He'fei 230026, China
Venue:
Computational Intelligence and Security
Year:
2007

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Abstract

Averaged One-Dependence Estimators [1], simply AODE, is a recently proposed algorithm which weakens the attribute independence assumption of naïve Bayes by averaging all the probability estimates of a collection of one-dependence estimators and demonstrates significantly high classification accuracy. In this paper, we study the selective AODE problem and proposed a Cross-Entropy based method to search the optimal subset over the whole one-dependence estimators. We experimentally test our algorithm in term of classification accuracy, using the 36 UCI data sets recommended by Weka, and compare it to C4.5[5], naïve Bayes, CL-TAN[6], HNB[7], AODE and LAODE[3]. The experiment results show that our method significantly outperforms all the other algorithms used to compare, and remarkably reduces the number of one-dependence estimators used compared to AODE.