Paper Details: Downloads: 493
Serial Number: P1121133730
Title: Enhancing Particle Swarm Optimization for Feature Combination Optimization
Authors: Li-Fei Chena and Kun-Huang Chenb and Fang-Fang Wangb
Abstract: Searching for an optimal feature combination from a high-dimensional feature space is an NP-complete problem. Traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to solve the feature selection problem efficiently. This study proposes an enhanced particle swarm optimization algorithm to avoid local optimal problem. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms.
Keywords: Feature combination optimization, particle swarm optimization, sequential search algorithms
Journal/Conference: ICGST Conference on Computer Science and Engineering, CSE-11
Volume:
Issue:
Submission Date: 8/15/2011 12:00:00 AM
Review Date: 10/3/2011 7:09:18 PM
Publishing Date: 12/19/2011 12:00:00 AM
Article Downloads: 493
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