Multiple Reference Points-Based Decomposition for Multiobjective Feature Selection in Classification: Static and Dynamic Mechanisms

Feature selection is an important task in machine learning that has two main objectives: 1) reducing dimensionality and 2) improving learning performance. Feature selection can be considered a multiobjective problem. However, it has its problematic characteristics, such as a highly discontinuous Par...

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Published in:	IEEE transactions on evolutionary computation Vol. 24; no. 1; pp. 170 - 184
Main Authors:	Nguyen, Bach Hoai, Xue, Bing, Andreae, Peter, Ishibuchi, Hisao, Zhang, Mengjie
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation algorithms Classification Decomposition Discontinuity Evolutionary algorithms Feature extraction feature selection Heuristic algorithms Machine learning multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization Multiple objective analysis Optimization Pareto optimization partially conflicting Shape effects Sociology Statistics Task analysis
ISSN:	1089-778X, 1941-0026
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Abstract	Feature selection is an important task in machine learning that has two main objectives: 1) reducing dimensionality and 2) improving learning performance. Feature selection can be considered a multiobjective problem. However, it has its problematic characteristics, such as a highly discontinuous Pareto front, imbalance preferences, and partially conflicting objectives. These characteristics are not easy for existing evolutionary multiobjective optimization (EMO) algorithms. We propose a new decomposition approach with two mechanisms (static and dynamic) based on multiple reference points under the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to address the above-mentioned difficulties of feature selection. The static mechanism alleviates the dependence of the decomposition on the Pareto front shape and the effect of the discontinuity. The dynamic one is able to detect regions in which the objectives are mostly conflicting, and allocates more computational resources to the detected regions. In comparison with other EMO algorithms on 12 different classification datasets, the proposed decomposition approach finds more diverse feature subsets with better performance in terms of hypervolume and inverted generational distance. The dynamic mechanism successfully identifies conflicting regions and further improves the approximation quality for the Pareto fronts.
AbstractList	Feature selection is an important task in machine learning that has two main objectives: 1) reducing dimensionality and 2) improving learning performance. Feature selection can be considered a multiobjective problem. However, it has its problematic characteristics, such as a highly discontinuous Pareto front, imbalance preferences, and partially conflicting objectives. These characteristics are not easy for existing evolutionary multiobjective optimization (EMO) algorithms. We propose a new decomposition approach with two mechanisms (static and dynamic) based on multiple reference points under the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to address the above-mentioned difficulties of feature selection. The static mechanism alleviates the dependence of the decomposition on the Pareto front shape and the effect of the discontinuity. The dynamic one is able to detect regions in which the objectives are mostly conflicting, and allocates more computational resources to the detected regions. In comparison with other EMO algorithms on 12 different classification datasets, the proposed decomposition approach finds more diverse feature subsets with better performance in terms of hypervolume and inverted generational distance. The dynamic mechanism successfully identifies conflicting regions and further improves the approximation quality for the Pareto fronts.
Author	Xue, Bing Zhang, Mengjie Andreae, Peter Ishibuchi, Hisao Nguyen, Bach Hoai
Author_xml	– sequence: 1 givenname: Bach Hoai orcidid: 0000-0002-6930-6863 surname: Nguyen fullname: Nguyen, Bach Hoai email: hoai.bach.nguyen@ecs.vuw.ac.nz organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 2 givenname: Bing orcidid: 0000-0002-4865-8026 surname: Xue fullname: Xue, Bing email: bing.xue@ecs.vuw.ac.nz organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 3 givenname: Peter surname: Andreae fullname: Andreae, Peter email: peter.andreae@ecs.vuw.ac.nz organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 4 givenname: Hisao orcidid: 0000-0001-9186-6472 surname: Ishibuchi fullname: Ishibuchi, Hisao email: hisaoi@cs.osakafu-u.ac.jp organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 5 givenname: Mengjie orcidid: 0000-0003-4463-9538 surname: Zhang fullname: Zhang, Mengjie email: mengjie.zhang@ecs.vuw.ac.nz organization: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
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SubjectTerms	Algorithms Approximation algorithms Classification Decomposition Discontinuity Evolutionary algorithms Feature extraction feature selection Heuristic algorithms Machine learning multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization Multiple objective analysis Optimization Pareto optimization partially conflicting Shape effects Sociology Statistics Task analysis
Title	Multiple Reference Points-Based Decomposition for Multiobjective Feature Selection in Classification: Static and Dynamic Mechanisms
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