Many-objective optimization of feature selection based on two-level particle cooperation
•Feature selection (FS) of high-dimensional data is reformulated as a many-objective optimization problem (MaOP) , consisting of three objectives to be minimized simultaneously.•To solve the formulated problem, we developed a PSO-based algorithm to search for the Pareto optimal solutions with two-le...
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| Vydáno v: | Information sciences Ročník 532; s. 91 - 109 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.09.2020
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| Témata: | |
| ISSN: | 0020-0255, 1872-6291 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •Feature selection (FS) of high-dimensional data is reformulated as a many-objective optimization problem (MaOP) , consisting of three objectives to be minimized simultaneously.•To solve the formulated problem, we developed a PSO-based algorithm to search for the Pareto optimal solutions with two-level particle cooperation under the MOEA/D framework.•We made a systematical comparison between these the proposed methods and some state-of-the-art single objective and other MaOP FS approaches and the results demonstrate the efficacy of our proposed methods both in classification accuracy on the test data and the size of the feature subset.
Feature selection (FS) plays a crucial role in classification, which aims to remove redundant and irrelevant data features.unknown However, for high-dimensional FS problems, Pareto optimal solutions are usually sparse, signifying that most of the decision variables are zero. Solving such problems using most existing evolutionary algorithms is difficult. In this paper, we reformulate FS as a many-objective optimization problem comprising three objectives to be minimized. To solve this problem, we proposed a binary particle swarm optimization with a two-level particle cooperation strategy. In the first level, to maintain rapid convergence, randomly generated ordinary particles and strict particles filtered by ReliefF are combined as the initialized particles. In the second level, under the decomposition multiobjective optimization framework, cooperation between particles is conducted during the update process to search for Pareto solutions more efficiently. In addition, a many-objective reset operation is proposed to enable the algorithm to jump out of the local optimum. Experimental studies on 10 real-world benchmark data sets revealed that our proposed algorithm could effectively reduce the number of features and achieve a competitive classification accuracy compared with some state-of-the-art evolutionary FS methods and non-evolutionary approaches. |
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| ISSN: | 0020-0255 1872-6291 |
| DOI: | 10.1016/j.ins.2020.05.004 |