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- Title
Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search.
- Authors
Fong, Simon; Yan Zhuang; Rui Tang; Xin-She Yang; Suash Deb
- Abstract
Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithmto facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.
- Subjects
OPTIMAL control theory; HIGH-dimensional model representation; MATHEMATICAL models; SET theory; MATHEMATICAL optimization; STOCHASTIC analysis; METAHEURISTIC algorithms
- Publication
Journal of Applied Mathematics, 2013, p1
- ISSN
1110-757X
- Publication type
Article
- DOI
10.1155/2013/590614