scholarly journals Feature Selection Based on a Large-Scale Many-Objective Evolutionary Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yue Li ◽  
Zhiheng Sun ◽  
Xin Liu ◽  
Wei-Tung Chen ◽  
Der-Juinn Horng ◽  
...  
Keyword(s):  
Feature Selection ◽  
Evolutionary Algorithm ◽  
Large Scale ◽  
Optimization Problem ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Worst Case ◽  
Research Fields ◽  
Proposed Model ◽  
Polynomial Mutation

The feature selection problem is a fundamental issue in many research fields. In this paper, the feature selection problem is regarded as an optimization problem and addressed by utilizing a large-scale many-objective evolutionary algorithm. Considering the number of selected features, accuracy, relevance, redundancy, interclass distance, and intraclass distance, a large-scale many-objective feature selection model is constructed. It is difficult to optimize the large-scale many-objective feature selection optimization problem by using the traditional evolutionary algorithms. Therefore, this paper proposes a modified vector angle-based large-scale many-objective evolutionary algorithm (MALSMEA). The proposed algorithm uses polynomial mutation based on variable grouping instead of naive polynomial mutation to improve the efficiency of solving large-scale problems. And a novel worst-case solution replacement strategy using shift-based density estimation is used to replace the poor solution of two individuals with similar search directions to enhance convergence. The experimental results show that MALSMEA is competitive and can effectively optimize the proposed model.

Hybrid binary Sine Cosine Algorithm and Ant Lion Optimization (SCALO) approaches for feature selection problem

2020 ◽  
Vol 09 (01) ◽  
pp. 1950021
Author(s):  
Rahul Hans ◽  
Harjot Kaur
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Transfer Functions ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Ant Lion Optimization ◽  
Ant Lion Optimizer ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Better Than

These days, a massive quantity of data is produced online and is incorporated into a variety of datasets in the form of features, however there are lot of features in these datasets that may not be relevant to the problem. In this perspective, feature selection aids to improve the classification accuracy with lesser number of features, which can be well thought-out as an optimization problem. In this paper, Sine Cosine Algorithm (SCA) hybridized with Ant Lion Optimizer (ALO) to form a hybrid Sine Cosine Ant Lion Optimizer (SCALO) is proposed. The proposed algorithm is mapped to its binary versions by using the concept of transfer functions, with the objective to eliminate the inappropriate features and to enhance the accuracy of the classification algorithm (or in any case remains the same). For the purpose of experimentation, this research considers 18 diverse datasets and moreover, the performance of the binary versions of SCALO is compared with some of the latest metaheuristic algorithms, on the basis of various criterions. It can be observed that the binary versions of SCALO perform better than the other algorithms on various evaluation criterions for solving feature selection problem.

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.

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.

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