An advanced ACO algorithm for feature subset selection

doi:10.1016/j.neucom.2014.06.067

Return

An advanced ACO algorithm for feature subset selection

2015-01-01

242

PRE

Hossein Nezamabadi–pour

DOI：10.1016/j.neucom.2014.06.067

Original

Original request for help

Save

Abstract

En 中文

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are treated as graph nodes to construct a graph model and are fully connected to each other. In this graph, each node has two sub-nodes, one for selecting and the other for deselecting the feature. Ant colony algorithm is used to select nodes while ants should visit all features. The use of several statistical measures is examined as the heuristic function for visibility of the edges in the graph. At the end of a tour, each ant has a binary vector with the same length as the number of features, where 1 implies selecting and 0 implies deselecting the corresponding feature. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), CatfishBPSO, Improved Binary Gravitational Search Algorithm (IBGSA), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods. (C) 2014 Elsevier B.V. All rights reserved.

Keywords:

Feature selection

Wrapper

Ant colony optimization (ACO)

Binary ACO

Classification

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.