کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
562619 | 875419 | 2013 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Efficient ant colony optimization for image feature selection Efficient ant colony optimization for image feature selection](/preview/png/562619.png)
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.
► A feature selection algorithm based on ant colony optimization is presented.
► The algorithm can obtain higher processing speed than other existing methods.
► The algorithm can select a smaller feature set than other existing methods.
► Higher quality classification results are obtained using such smaller feature set.
► The advantages of the algorithm are proved empirically.
Journal: Signal Processing - Volume 93, Issue 6, June 2013, Pages 1566–1576