An advanced ACO algorithm for feature subset selection

Kashef, Shima; Nezamabadi‐pour, Hossein

doi:10.1016/j.neucom.2014.06.067

Cited by 285 publications

(136 citation statements)

References 37 publications

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“…The ACO algorithm is based on the ants' foraging behavior in nature to find the optimal path. Graphically, it has been applied widely in various fields [21]. In the ant colony algorithm, the intelligence of a single ant is limited, but the ants can accomplish complex tasks via group cooperation.…”

Section: Representative Schemes For Comparisonmentioning

confidence: 99%

Reliable Ant Colony Routing Algorithm for Dual‐Channel Mobile Ad Hoc Networks

Wang

et al. 2018

Wireless Communications and Mobile Computing

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For the problem of poor link reliability caused by high-speed dynamic changes and congestion owing to low network bandwidth in ad hoc networks, an ant colony routing algorithm, based on reliable path under dual-channel condition (DSAR), is proposed. First, dual-channel communication mode is used to improve network bandwidth, and a hierarchical network model is proposed to optimize the dual-layer network. Thus, we reduce network congestion and communication delay. Second, a comprehensive reliable path selection strategy is designed, and the reliable path is selected ahead of time to reduce the probability of routing restart. Finally, the ant colony algorithm is used to improve the adaptability of the routing algorithm to changes of network topology. Simulation results show that DSAR improves the reliability of routing, packet delivery, and throughput.

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Section: Representative Schemes For Comparisonmentioning

confidence: 99%

Reliable Ant Colony Routing Algorithm for Dual‐Channel Mobile Ad Hoc Networks

Wang

et al. 2018

Wireless Communications and Mobile Computing

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“…In [30] an advanced binary ACO is presented. Each node in the graph has two sub-nodes, one for selecting and the other for deselecting the features.…”

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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“…Ant colony optimization (ACO [36]) with some modifications termed ABACO is used for feature selection [10]. Graph regularized Non Matrix Factorization (GNMF) is developed for feature selection [7].…”

Section: Literature Surveymentioning

confidence: 99%

“…Eliminating redundant, noisy and irrelevant features from datasets is defined as Dimensionality Reduction. Dimensionality reduction is used in face image dataset [6,7,8,9], micro array dataset and speech signals [6,7], digit images [6,7,8], letter images [8,10] for classification or clustering. Feature selection refers to selecting a subset of features from a complete set of features in a dataset.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Feature Selection Algorithms

Kumar¹

2015

IJRITCC

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Abstract-One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and fe ature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.

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An advanced ACO algorithm for feature subset selection

Cited by 285 publications

References 37 publications

Reliable Ant Colony Routing Algorithm for Dual‐Channel Mobile Ad Hoc Networks

Reliable Ant Colony Routing Algorithm for Dual‐Channel Mobile Ad Hoc Networks

Hybrid ACO-PSO Based Approaches for Feature Selection

A Survey on Feature Selection Algorithms

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