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Mathematical Problems in Engineering
Volume 2014, Article ID 470821, 11 pages
http://dx.doi.org/10.1155/2014/470821
Research Article

Extracting Credible Dependencies for Averaged One-Dependence Estimator Analysis

1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
2State Key Laboratory of Computer Science, Beijing 100080, China
3School of Mathematics and Information, Shanghai Lixin University of Commerce, Shanghai 210620, China
4Medical College, Jilin University, Changchun 130021, China

Received 8 April 2014; Revised 25 May 2014; Accepted 26 May 2014; Published 17 June 2014

Academic Editor: Yang Xu

Copyright © 2014 LiMin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Of the numerous proposals to improve the accuracy of naive Bayes (NB) by weakening the conditional independence assumption, averaged one-dependence estimator (AODE) demonstrates remarkable zero-one loss performance. However, indiscriminate superparent attributes will bring both considerable computational cost and negative effect on classification accuracy. In this paper, to extract the most credible dependencies we present a new type of seminaive Bayesian operation, which selects superparent attributes by building maximum weighted spanning tree and removes highly correlated children attributes by functional dependency and canonical cover analysis. Our extensive experimental comparison on UCI data sets shows that this operation efficiently identifies possible superparent attributes at training time and eliminates redundant children attributes at classification time.