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International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 460641, 15 pages
http://dx.doi.org/10.1155/2013/460641
Research Article

Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network

1Department of Computer and Information Science, University of Macau, Taipa, Macau
2School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
3Department of Electronic Engineering, Beijing University of Technology, Beijing 100022, China

Received 6 October 2012; Accepted 18 October 2012

Academic Editor: Sabah Mohammed

Copyright © 2013 Hang Yang 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

Standard classification algorithms are often inaccurate when used in a wireless sensor network (WSN), where the observed data occur in imbalanced classes. The imbalanced data classification problem occurs when the number of samples in one class, usually the class of interest, is much lower than the number in the other classes. Many classification models have been studied in the data-mining research community. However, they all assume that the input data are stationary and bounded in size, so that resampling techniques and postadjustment by measuring the classification cost can be applied. In this paper, we devise a new scheme that extends a popular stream classification algorithm to the analysis of WSNs for reducing the adverse effects of the imbalanced class in the data. This new scheme is resource light at the algorithm level and does not require any data preprocessing. It uses weighted naïve Bayes predictors at the decision tree leaves to effectively reduce the impact of imbalanced classes. Experiments show that our modified algorithm outperforms the original stream classification algorithm.