Improved binary PSO for feature selection using gene expression data

Chuang, Li‐Yeh; Chang, Hsueh-Wei; Tu, Chung-Jui; Yang, Cheng‐Hong

doi:10.1016/j.compbiolchem.2007.09.005

Cited by 531 publications

(270 citation statements)

References 12 publications

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“…In particular, PSO, as a popular metaheuristics, has also been widely adopted for feature selection [40]. Chuang et al have developed an improved binary PSO algorithm for feature selection using gene expression data [12]. Wang et al have suggested a filter feature selection approach based on rough set and PSO [38].…”

Section: Metaheuristics For Feature Selectionmentioning

confidence: 99%

Feature selection for high-dimensional classification using a competitive swarm optimizer

2016

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When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance. Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to performing feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based

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Section: Metaheuristics For Feature Selectionmentioning

confidence: 99%

Feature selection for high-dimensional classification using a competitive swarm optimizer

2016

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“…Chuang et al [26] developed a strategy for gbest in PSO for feature selection in which gbest will be reset to zero if it maintains the same value after several iterations. Liu et al [27] introduced a multi swarm PSO algorithm to search for the optional feature subset and optimize the parameters of SVM simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Optimal Feature Selection Algorithm for High Dimensional Data Sets Using Particle Swarm Optimization

2017

IJLTET

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“…Niiniskorpi et al [35] show that the PSO is effective in the identification of high-performing voxel subsets for functional magnetic resonance imaging (fMRI) volume classification. Li-Yeh Chuang et al [36] used an improved binary particle swarm optimization (IBPSO) to implement feature selection, and the K-nearest neighbor (K-NN) method serves as an evaluator of the IBPSO for gene expression data classification problems. Moreover, in [37] a PSO based feature selection algorithm was proposed for an automatic speaker verification system.…”

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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Improved binary PSO for feature selection using gene expression data

Cited by 531 publications

References 12 publications

Feature selection for high-dimensional classification using a competitive swarm optimizer

Feature selection for high-dimensional classification using a competitive swarm optimizer

Optimal Feature Selection Algorithm for High Dimensional Data Sets Using Particle Swarm Optimization

Hybrid ACO-PSO Based Approaches for Feature Selection

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