Overview of Particle Swarm Optimisation for Feature Selection in Classification

Tran, Binh; Xue, Bing; Zhang, Mengjie

doi:10.1007/978-3-319-13563-2_51

Cited by 41 publications

(26 citation statements)

References 42 publications

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“…By removing the irrelevant and redundant features, feature selection can simplify the structure of the model, improve the generalization capacity, and enhance the efficiency in training and responding [43]. Feature selection is non-trivial, as it is essentially a combinatorial optimization problem [44].…”

Section: A Feature Selection and Feature Extractionmentioning

confidence: 99%

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Guo

Jin

Ding

et al. 2019

IEEE Trans. Cybern.

170

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Gaussian processes (GPs) are the most popular model used in surrogate-assisted evolutionary optimization of computationally expensive problems, mainly because GPs are able to measure the uncertainty of the estimated fitness values, based on which certain infill sampling criteria can be used to guide the search and update the surrogate model. However, the computation time for constructing GPs may become excessively long when the number of training samples increases, which makes it inappropriate to use them as surrogates in evolutionary optimization. To address this issue, this paper proposes to use ensembles as surrogates and infill criteria for model management in evolutionary optimization. A heterogeneous ensemble consisting of a least square support vector machine and two radial basis function networks is constructed to enhance the reliability of ensembles for uncertainty estimation. In addition to the original decision variables, a selected subset of the decision variables and a set of transformed variables are used as inputs of the heterogeneous ensemble to further promote the diversity of the ensemble. The proposed heterogeneous ensemble is compared with a GP and a homogeneous ensemble for infill sampling criteria in evolutionary multiobjective optimization. Experimental results demonstrate that the heterogeneous ensemble is competitive in performance compared with GPs and much more scalable in computational complexity to the increase in search dimension.

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Section: A Feature Selection and Feature Extractionmentioning

confidence: 99%

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Guo

Jin

Ding

et al. 2019

IEEE Trans. Cybern.

170

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show abstract

“…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

Self Cite

153

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“…A review of PSO algorithms and their variations are presented by the authors Tran et al in their paper [17]. Current issues and challenges for future research are also discussed.…”

Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

A Modified Binary PSO Based Feature Selection for Automatic Lesion Detection in Mammograms

K.U¹,

S²,

Ramachandran³

2018

IJCSIT

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This paper presents an effective feature selection method that can be applied to build a computer aided diagnosis system for breast cancer in order to discriminate between healthy, benign and malignant parenchyma. Determining the optimal feature set from a large set of original features is an important preprocessing step which removes irrelevant and redundant features and thus improves computational efficiency, classification accuracy and also simplifies the classifier structure. A modified binary particle swarm optimized feature selection method (MBPSO)has been proposed where k-Nearest Neighbour algorithm with leave-one-out cross validation serves as the fitness function. Digital mammograms obtained from Regional Cancer Centre, Thiruvananthapuram and the mammograms from web accessible mini-MIAS database has been used as the dataset for this experiment. Region of interests from the mammograms are automatically detected and segmented. A total of 117 shape, texture and histogram features are extracted from the ROIs. Significant features are selected using the proposed feature selection method.Classification is performed using feed forward artificial neural networks with back propagation learning. Receiver operating characteristics (ROC) and confusion matrix are used to evaluate the performance. Experimental results show that the modified binary PSO feature selection method not only obtains better classification accuracy but also simplifies the classification process as compared to full set of features. The performance of the modified BPSO is found to be at par with other widely used feature selection techniques.

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Overview of Particle Swarm Optimisation for Feature Selection in Classification

Cited by 41 publications

References 42 publications

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

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

A Modified Binary PSO Based Feature Selection for Automatic Lesion Detection in Mammograms

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