An advanced ACO algorithm for feature subset selection

• Published in: Neurocomputing (Amsterdam) Vol. 147; pp. 271 - 279
• Main Authors: Kashef, Shima, Nezamabadi-pour, Hossein
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 05.01.2015; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Ant colony optimization (ACO); Applied sciences; Binary ACO; Classification; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Feature selection; Memory organisation. Data processing; Software; Theoretical computing; Wrapper; Wrapper; Binary ACO; Feature selection; Ant colony optimization (ACO); Classification; Artificial life; Data mining; Task analysis; Graph decomposition; Electronic packaging; Selection criterion; Swarm intelligence; Ant colony optimization; Data analysis; Edge(graph); Gravitation; Statistical analysis; Redundancy; Information retrieval; Data processing; Graph theory; Particle swarm optimization; Genetic algorithm; Surface mount technology; Pattern classification; Soldered joint; Heuristic method; Visibility; Signal to noise ratio
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.06.067

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.