A new hybrid ant colony optimization algorithm for feature selection

Kabir, Md. Monirul; Shahjahan, Md.; Murase, Kazuyuki

doi:10.1016/j.eswa.2011.09.073

Cited by 208 publications

(86 citation statements)

References 39 publications

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“…Some of them use hybrid techniques (filter and wrapper) to estimate the heuristic information and overall performance. For example, Kabir et al [21,22] propose a new hybrid ACO algorithm for feature selection, called ACOFS. ACOFS uses a hybrid search technique that combines the advantages of wrapper and filter approaches.…”

Section: Aco Based Feature Selectionmentioning

confidence: 99%

Hybrid ACO-PSO Based Approaches for Feature Selection

Menghour¹,

Souici-Meslati²

2016

IJIES

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Abstract:Feature selection attempts to find the most discriminative information aiming to design an accurate learning system. Feature selection has been the focus of interest for a long time and many works had been done. Recently, the tendency of research in this domain is oriented to the bio-inspired methods. In this paper, we propose hybrid bio-inspired approaches applied to the feature selection problem. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with simple bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization and genetic algorithm. Our experimental results show the efficiency of the proposed approaches in the reduction of selected features number and improvement of classification performance.

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Section: Aco Based Feature Selectionmentioning

confidence: 99%

Hybrid ACO-PSO Based Approaches for Feature Selection

Menghour¹,

Souici-Meslati²

2016

IJIES

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“…The hybrid approach attempts to utilize the complementary strengths of the wrapper and filter approaches [23]. The meta-heuristics (or, global search approaches) attempt to search a salient feature subset in a full feature space in order to find a high-quality solution using mutual cooperation of individual agents, such as, genetic algorithm, ant colony optimization, and so on [64]. Now, the schematic diagrams of how the filter, wrapper, and hybrid approaches find relevant (salient) features are given in Figures 3(a,b,c).…”

Section: Features Classes Examples Partition Setsmentioning

confidence: 99%

Ant Colony Optimization Toward Feature Selection

Kabir¹,

Shahjahan²,

Murase³

2013

Ant Colony Optimization - Techniques and Applications

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“…The objective of feature selection is to identify important features in the dataset and remove any other irrelevant features and redundant information [10].…”

Section: Introductionmentioning

confidence: 99%

A hybrid search method of wrapper feature selection by chaos particle swarm optimization and local search

Javidi¹,

Emami²

2016

Turk J Elec Eng & Comp Sci

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Finding a subset of features from a large dataset is a problem that arises in many fields of study. Since the increasing number of features has extended the computational cost of a system, it is necessary to design and implement a system with the least number of features. The purpose of feature selection is to find the best subset of features from the original ones. The result of the best selection is improving the computational cost and the accuracy of the prediction.A large number of algorithms have been proposed for feature subset selection. In this paper, we propose a wrapper feature selection algorithm for a classification that is based on chaos theory, binary particle swarm optimization, and local search. In the proposed algorithm, the nearest neighbor algorithm is used for the evaluation phase.

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A new hybrid ant colony optimization algorithm for feature selection

Cited by 208 publications

References 39 publications

Hybrid ACO-PSO Based Approaches for Feature Selection

Hybrid ACO-PSO Based Approaches for Feature Selection

Ant Colony Optimization Toward Feature Selection

A hybrid search method of wrapper feature selection by chaos particle swarm optimization and local search

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