Efficient ant colony optimization for image feature selection

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• 作者：Bolun Chen^a ; ^{Chenbolun1986@gmail.com} ; Ling Chen^b ; ^c ; ^{lchen@yzu.edu.cn} ; Yixin Chen^d ; ^{chen@cse.wustl.edu}
• 关键词：Ant colony optimization ; Dimensionality reduction ; Feature selection ; Image classification
• 刊名：Signal Processing
• 出版年：2013
• 出版时间：June, 2013
• 年：2013
• 卷：93
• 期：6
• 页码：1566-1576
• 全文大小：940 K

文摘

Feature selection (FS) is an important task which can significantly affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ant colony optimization (ACO). For n features, existing ACO-based feature selection methods need to traverse a complete graph with O(n²) edges. However, we propose a novel algorithm in which the artificial ants traverse on a directed graph with only O(2n) arcs. The algorithm incorporates the classification performance and feature set size into the heuristic guidance, and selects a feature set with small size and high classification accuracy. We perform extensive experiments on two large image databases and 15 non-image datasets to show that our proposed algorithm can obtain higher processing speed as well as better classification accuracy using a smaller feature set than other existing methods.