Application of Global Optimization Methods for Feature Selection and Machine Learning
Joint Authors
Wu, Shaohua
Hu, Yong
Wang, Wei
Feng, Xinyong
Shu, Wanneng
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-14
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The feature selection process constitutes a commonly encountered problem of global combinatorial optimization.
The process reduces the number of features by removing irrelevant and redundant data.
This paper proposed a novel immune clonal genetic algorithm based on immune clonal algorithm designed to solve the feature selection problem.
The proposed algorithm has more exploration and exploitation abilities due to the clonal selection theory, and each antibody in the search space specifies a subset of the possible features.
Experimental results show that the proposed algorithm simplifies the feature selection process effectively and obtains higher classification accuracy than other feature selection algorithms.
American Psychological Association (APA)
Wu, Shaohua& Hu, Yong& Wang, Wei& Feng, Xinyong& Shu, Wanneng. 2013. Application of Global Optimization Methods for Feature Selection and Machine Learning. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1008785
Modern Language Association (MLA)
Wu, Shaohua…[et al.]. Application of Global Optimization Methods for Feature Selection and Machine Learning. Mathematical Problems in Engineering No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-1008785
American Medical Association (AMA)
Wu, Shaohua& Hu, Yong& Wang, Wei& Feng, Xinyong& Shu, Wanneng. Application of Global Optimization Methods for Feature Selection and Machine Learning. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1008785
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1008785