Dimensional reduction based on artificial bee colony for classification problems
Conference proceedings article
Authors/Editors
Strategic Research Themes
No matching items found.
Publication Details
Author list: Prasartvit T., Kaewkamnerdpong B., Achalakul T.
Publisher: Springer
Publication year: 2011
Volume number: 6840 LNBI
Start page: 168
End page: 175
Number of pages: 8
ISBN: 9783642245527
ISSN: 0302-9743
Languages: English-Great Britain (EN-GB)
Abstract
High dimensionality of data is a limiting factor to data processing in many fields. It causes ambiguousness in identifying significant factors for data analysis. Dimension reduction is needed to separate irrelevant data from the desired data. This research proposes a novel method for dimension reduction based on artificial bee colony (ABC). The method employs swarm intelligence based on bee foraging model in order to select features that allow us to generate subsets of dimensions from the original high-dimensional data while the resulting subsets satisfy the defined objective. Support vector machine (SVM) is used in this study as fitness evaluation of ABC in classification problems. To evaluate our method, we tested it with five datasets and compared it with other dimension reduction algorithms. The result of this study shows that using ABC and SVM is suitable for reducing the dimension of data. Moreover, this approach provides efficient classification with high accuracy. ฉ 2012 Springer-Verlag.
Keywords
No matching items found.
Contact Information