Improved PSO for Feature Selection on High-Dimensional Datasets

Tran, Binh; Xue, Bing; Zhang, Mengjie

doi:10.1007/978-3-319-13563-2_43

Cited by 34 publications

(26 citation statements)

References 19 publications

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“…Multi-Objective Wrapper [10], [42], [70], [133], [134], [135], [136], [137], [138] [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160] [29], [161], [162] Filter [34], [163], [164], [165], [166], [167], [168], [169], [170] [171], [172], [173], [174] Combined [11], [33], [175], [176], [177] C. PSO for Feature Selection…”

Section: Table III Categorisation Of Pso Approaches Single Objectivementioning

confidence: 99%

“…Xue et al [158] considered the number of features when updating pbest and gbest during the search process of PSO, which could further reduce the number of features over the traditional updating pbest and gbest mechanism without deteriorating the classification performance. Tran et al [156] used the gbest resetting mechanism in [140] to reduce the number of features and performed a local search process on pbest to increase the classification performance. Each evaluation in the local search was sped up by calculating fitness based only on the features being changed (from selected to not selected or from not selected to selected) instead of based on all the selected features.…”

Section: Table III Categorisation Of Pso Approaches Single Objectivementioning

confidence: 99%

See 1 more Smart Citation

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

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1,367

635

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Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Table III Categorisation Of Pso Approaches Single Objectivementioning

confidence: 99%

Section: Table III Categorisation Of Pso Approaches Single Objectivementioning

confidence: 99%

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

Self Cite

1,367

635

View full text Add to dashboard Cite

show abstract

“…In PSO, individual particles are moving in the search space and they are communicating with each other via iterations in order to search for optimal solutions (Tran et al, 2014). If a search space of D-dimensions is assumed, then the ith swarm particle can have a Ddimensional position vector represented by Xi = [1, 2,...;…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The inertia weight (w) in "Equation 1" acts to gradually reduce the particles velocity and hence controlling the swarms. The value of w is usually located between 0.4 and 0.9, whereas the random variables rand 1 and rand 2 are uniformly distributed between 0 and 1 (Tran et al, 2014). As such, the velocities of particles are bounded within…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

Hameed¹,

Muhammad²,

Hassan³

et al. 2018

Journal of Computer Science

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In this study, a three-phase hybrid approach is proposed for the selection and classification of high dimensional microarray data. The method uses Pearson's Correlation Coefficient (PCC) in combination with Binary Particle Swarm Optimization (BPSO) or Genetic Algorithm (GA) along with various classifiers, thereby forming a PCC-BPSO/GA-multi classifiers approach. As such, five various classifiers are employed in the final stage of the classification. It was noticed that the PCC filter showed a remarkable improvement in the classification accuracy when it was combined with BPSO or GA. This positive impact was seen to be varied for different datasets based on the final applied classifier. The performance of various combination of the hybrid technique was compared in terms of accuracy and number of selected genes. In addition to the fact that BPSO is working faster than GA, it was noticed that BPSO has better performance than GA when it is combined with PCC feature selection.

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“…PSO algorithm is widely used as a global optimization algorithm, which has the characteristics of simple programming, high efficiency, and fast computing speed [15][16][17]. The solution space is considered as a multidimension search space.…”

Section: Ipso Algorithmmentioning

confidence: 99%

High Precision Clock Bias Prediction Model in Clock Synchronization System

Liu

Chen

Liu

et al. 2016

Mathematical Problems in Engineering

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Time synchronization is a fundamental requirement for many services provided by a distributed system. Clock calibration through the time signal is the usual way to realize the synchronization among the clocks used in the distributed system. The interference to time signal transmission or equipment failures may bring about failure to synchronize the time. To solve this problem, a clock bias prediction module is paralleled in the clock calibration system. And for improving the precision of clock bias prediction, the firstorder grey model with one variable (GM(1, 1)) model is proposed. In the traditional GM(1, 1) model, the combination of parameters determined by least squares criterion is not optimal; therefore, the particle swarm optimization (PSO) is used to optimize GM(1, 1) model. At the same time, in order to avoid PSO getting stuck at local optimization and improve its efficiency, the mechanisms that double subgroups and nonlinear decreasing inertia weight are proposed. In order to test the precision of the improved model, we design clock calibration experiments, where time signal is transferred via radio and wired channel, respectively. The improved model is built on the basis of clock bias acquired in the experiments. The results show that the improved model is superior to other models both in precision and in stability. The precision of improved model increased by 66.4%∼76.7%.

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Improved PSO for Feature Selection on High-Dimensional Datasets

Cited by 34 publications

References 19 publications

A Survey on Evolutionary Computation Approaches to Feature Selection

A Survey on Evolutionary Computation Approaches to Feature Selection

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

High Precision Clock Bias Prediction Model in Clock Synchronization System

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