scholarly journals Insights into the Feature Selection Problem Using Local Optima Networks

2019 ◽  
pp. 147-162 ◽  
Author(s):  
Werner Mostert ◽  
Katherine M. Malan ◽  
Gabriela Ochoa ◽  
Andries P. Engelbrecht
Keyword(s):  
Feature Selection ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Local Optima ◽  
Local Optima Networks

scholarly journals Self-Adaptive particle swarm optimization for large-scale feature selection in classification

2021 ◽  
Author(s):  
Y Xue ◽  
Bing Xue ◽  
M Zl
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Solution Space ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Swarm Optimization ◽  
Local Optima ◽  
Scale Feature ◽  
Selection Problems ◽  
Self Adaptive

© 2019 Association for Computing Machinery. Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to fnd a suitable EC method and corresponding suitable parameter values for a given largescale feature selection problem if we want to solve it effectively and efciently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for largescale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classifcation accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems. © Xue 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'ACM Transactions on Knowledge Discovery from Data', https://dx.doi.org/10.1145/3340848.

scholarly journals Self-Adaptive particle swarm optimization for large-scale feature selection in classification

2021 ◽  
Author(s):  
Y Xue ◽  
Bing Xue ◽  
M Zl
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Solution Space ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Swarm Optimization ◽  
Local Optima ◽  
Scale Feature ◽  
Selection Problems ◽  
Self Adaptive

© 2019 Association for Computing Machinery. Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to fnd a suitable EC method and corresponding suitable parameter values for a given largescale feature selection problem if we want to solve it effectively and efciently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for largescale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classifcation accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems. © Xue 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'ACM Transactions on Knowledge Discovery from Data', https://dx.doi.org/10.1145/3340848.

scholarly journals The GRA-two algorithm for massive-scale feature selection problem in power system scenario classification and prediction

2021 ◽  
Vol 7 ◽  
pp. 293-303
Author(s):  
Yang Wang ◽  
Xinxiong Jiang ◽  
Faqi Yan ◽  
Yu Cai ◽  
Siyang Liao
Keyword(s):  
Feature Selection ◽  
Power System ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Scale Feature ◽  
Massive Scale

A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

scholarly journals Scatter Search for the Feature Selection Problem

2004 ◽  
pp. 517-525 ◽  
Author(s):  
Félix C. García López ◽  
Miguel García Torres ◽  
José A. Moreno Pérez ◽  
J. Marcos Moreno Vega
Keyword(s):  
Feature Selection ◽  
Scatter Search ◽  
Selection Problem ◽  
Feature Selection Problem
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