A Survey on Particle Swarm Optimization in Feature Selection

Kothari, Vipul; Anuradha, J.; Shah, Shreyak; Mittal, Prerit

doi:10.1007/978-3-642-29216-3_22

Cited by 43 publications

(26 citation statements)

References 8 publications

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“…In another study, Xue et al [36] integrated three new initialisation strategies and updating mechanisms to the PSO algorithm motivated by forward selection, backward selection and a combination of them. The detailed survey of PSO on feature selection can be found in [48,49]. ACO has also been used to solve FS problems, where nodes represent features, and the edges between nodes define the choice of the next feature in graphs [50].…”

Section: Existing Feature Selection Methodsmentioning

confidence: 99%

A binary ABC algorithm based on advanced similarity scheme for feature selection

Hançer

Xue

Karaboğa

et al. 2015

Applied Soft Computing

156

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Section: Existing Feature Selection Methodsmentioning

confidence: 99%

A binary ABC algorithm based on advanced similarity scheme for feature selection

Hançer

Xue

Karaboğa

et al. 2015

Applied Soft Computing

156

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“…Particle swarm optimization algorithm is a novel metaheuristic optimization algorithm inspired by the natural behavior of the flocking bird in their search process for finding the best particle [17]. The parameters of the PSO algorithm are determined to the default parameters in Weka to certify the correct behavior of it.…”

Section: Particle Swarm Optimization (Pso) Parametersmentioning

confidence: 99%

Feature Selection to Classify Healthcare Data using Wrapper Method with PSO Search

Saw¹,

Myint²

2019

IJITCS

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As a result of the rapid development of technology, data that contain a large number of features are produced from various applications such as biomedical, social media, face recognition, etc. Processing of these data is a challenging task to existing data mining and machine learning algorithms to make the decision. To reduce the size of the data for processing, a feature selection technique is needed. The feature selection is a well-known attribute selection or variable selection. The objective of the feature selection is to minimize the number of attributes contains in the dataset by eliminating the unwanted and repeated attributes to improve the classification accuracy and reduce the computation cost. Although various feature selection methods are proposed, in literature, to classify the healthcare data especially cancer diagnosis, finding an informative feature for medical datasets has still remained a challenging issue in the data mining and machine learning domain. Therefore, this paper presents a feature selection approach with the wrapper method (WFS) using particle swarm optimization (PSO) search to improve the accuracy of healthcare data classification. This work is evaluated on five benchmark medical datasets publicly available from the UCI machine learning repository. The experimental results showed that the WFS-PSO approach produces higher classification accuracy applied to different classification algorithms.

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“…Kothari et al [34] present a survey paper on particle swarm optimization in feature selection. The paper presents a comparative study of PSO implementations and reviews the success of PSO in various fields of science.…”

Section: Pso 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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A Survey on Particle Swarm Optimization in Feature Selection

Cited by 43 publications

References 8 publications

A binary ABC algorithm based on advanced similarity scheme for feature selection

A binary ABC algorithm based on advanced similarity scheme for feature selection

Feature Selection to Classify Healthcare Data using Wrapper Method with PSO Search

Hybrid ACO-PSO Based Approaches for Feature Selection

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