Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 590678, 12 pages
Research Article

A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification

1Database and Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 362763, Republic of Korea
2Graduate School of Professional Science Master, Chungbuk National University, Cheongju 362763, Republic of Korea
3Department of Computer Science, Namseoul University, Cheonan 331707, Republic of Korea
4School of Electronics & Computer Engineering, Chonnam National University, Gwangju 500757, Republic of Korea

Received 25 November 2014; Accepted 5 January 2015

Academic Editor: Sanghyuk Lee

Copyright © 2015 Yongjun Piao 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.


Ensemble data mining methods, also known as classifier combination, are often used to improve the performance of classification. Various classifier combination methods such as bagging, boosting, and random forest have been devised and have received considerable attention in the past. However, data dimensionality increases rapidly day by day. Such a trend poses various challenges as these methods are not suitable to directly apply to high-dimensional datasets. In this paper, we propose an ensemble method for classification of high-dimensional data, with each classifier constructed from a different set of features determined by partitioning of redundant features. In our method, the redundancy of features is considered to divide the original feature space. Then, each generated feature subset is trained by a support vector machine, and the results of each classifier are combined by majority voting. The efficiency and effectiveness of our method are demonstrated through comparisons with other ensemble techniques, and the results show that our method outperforms other methods.