A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification
Joint Authors
Piao, Yongjun
Piao, Minghao
Jin, Cheng Hao
Shon, Ho Sun
Chung, Ji-Moon
Hwang, Buhyun
Ryu, Keun Ho
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-19
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Piao, Yongjun& Piao, Minghao& Jin, Cheng Hao& Shon, Ho Sun& Chung, Ji-Moon& Hwang, Buhyun…[et al.]. 2015. A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1074219
Modern Language Association (MLA)
Piao, Yongjun…[et al.]. A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification. Mathematical Problems in Engineering No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1074219
American Medical Association (AMA)
Piao, Yongjun& Piao, Minghao& Jin, Cheng Hao& Shon, Ho Sun& Chung, Ji-Moon& Hwang, Buhyun…[et al.]. A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1074219
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074219