Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection

•Multiobjective unsupervised feature selection with many decision variables is tackled.•EEG signals for Brain–Computer Interface (BCI) applications are used as benchmarks.•Cooperative evolutionary algorithms for multiobjective optimization are given.•Parallel implementations obtain quality results i...

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Published in:	Expert systems with applications Vol. 42; no. 9; pp. 4239 - 4252
Main Authors:	Kimovski, Dragi, Ortega, Julio, Ortiz, Andrés, Baños, Raúl
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.06.2015
Subjects:	Computational efficiency Computing time Evolution Evolutionary Expert systems Feature selection High-dimensional data Human-computer interface Multi-objective clustering Optimization Parallel evolutionary algorithms Pattern recognition Speedup models Unsupervised classification High-dimensional data Speedup models Feature selection Multi-objective clustering Parallel evolutionary algorithms Unsupervised classification
ISSN:	0957-4174, 1873-6793
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Abstract	•Multiobjective unsupervised feature selection with many decision variables is tackled.•EEG signals for Brain–Computer Interface (BCI) applications are used as benchmarks.•Cooperative evolutionary algorithms for multiobjective optimization are given.•Parallel implementations obtain quality results in terms of hypervolume and speedup.•Superlinear speedups are justified by adjusting models to experimental results. Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain–Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile.
AbstractList	•Multiobjective unsupervised feature selection with many decision variables is tackled.•EEG signals for Brain–Computer Interface (BCI) applications are used as benchmarks.•Cooperative evolutionary algorithms for multiobjective optimization are given.•Parallel implementations obtain quality results in terms of hypervolume and speedup.•Superlinear speedups are justified by adjusting models to experimental results. Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain–Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile. Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain-Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile.
Author	Ortiz, Andrés Kimovski, Dragi Ortega, Julio Baños, Raúl
Author_xml	– sequence: 1 givenname: Dragi surname: Kimovski fullname: Kimovski, Dragi email: dragi.kimovski@uist.edu.mk organization: University of Information, Science & Technology, Ohrid, Macedonia – sequence: 2 givenname: Julio surname: Ortega fullname: Ortega, Julio email: jortega@ugr.es organization: Dept. Computer Architecture and Technology, CITIC, University of Granada, Spain – sequence: 3 givenname: Andrés surname: Ortiz fullname: Ortiz, Andrés email: aortiz@ic.uma.es organization: Dept. Communications Engineering, University of Malaga, Spain – sequence: 4 givenname: Raúl surname: Baños fullname: Baños, Raúl email: rbanos@ucam.edu organization: Dept. Business Administration and Management, Catholic University of Murcia, Spain
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Snippet	•Multiobjective unsupervised feature selection with many decision variables is tackled.•EEG signals for Brain–Computer Interface (BCI) applications are used as... Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed....
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SubjectTerms	Computational efficiency Computing time Evolution Evolutionary Expert systems Feature selection High-dimensional data Human-computer interface Multi-objective clustering Optimization Parallel evolutionary algorithms Pattern recognition Speedup models Unsupervised classification
Title	Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection
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