Result: An advanced ACO algorithm for feature subset selection

Title:
An advanced ACO algorithm for feature subset selection
Authors:
KASHEF, Shima, NEZAMABADI-POUR, Hossein
Source:
Neurocomputing (Amsterdam). 147:271-279
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 45 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse donnée, Data analysis, Análisis datos, Analyse statistique, Statistical analysis, Análisis estadístico, Arête graphe, Edge(graph), Arista gráfico, Assemblage brasage tendre, Soldered joint, Junta soldada, Critère sélection, Selection criterion, Criterio selección, Décomposition graphe, Graph decomposition, Descomposición grafo, Fouille donnée, Data mining, Busca dato, Gravitation, Gravitación, Intelligence en essaim, Swarm intelligence, Inteligencia de enjambre, Montage surface composant, Surface mount technology, Montaje superficie componente, Méthode heuristique, Heuristic method, Método heurístico, Optimisation PSO, Particle swarm optimization, Optimización PSO, Packaging électronique, Electronic packaging, Packaging electrónico, Rapport signal bruit, Signal to noise ratio, Relación señal ruido, Recherche information, Information retrieval, Búsqueda información, Redondance, Redundancy, Redundancia, Théorie graphe, Graph theory, Teoría grafo, Traitement donnée, Data processing, Tratamiento datos, Visibilité, Visibility, Visibilidad, Analyse tâche, Task analysis, Análisis de tareas, Classification forme, Pattern classification, Clasificación de patrones, Optimisation par colonies de fourmis, Ant colony optimization, Algoritmo de las hormigas, Vie artificielle, Artificial life, Vida artificial, Ant colony optimization (ACO), Binary ACO, Classification, Feature selection, Wrapper
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76619-133, Kerman, Iran, Islamic Republic of
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28836750
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28836750
Database:
PASCAL Archive

Further Information

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.