Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection
•Three candidate bi-objective key quality characteristic selection models are proposed.•A new hybrid method of a GA and direct multisearch is proposed for optimization.•The model with geometric mean measure performs better than the other two models.•The proposed method effectively addresses the prod...
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| Published in: | Information sciences Vol. 523; pp. 245 - 265 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.06.2020
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | •Three candidate bi-objective key quality characteristic selection models are proposed.•A new hybrid method of a GA and direct multisearch is proposed for optimization.•The model with geometric mean measure performs better than the other two models.•The proposed method effectively addresses the production data imbalance problem.•The optimization approach shows better search performance than benchmark methods.
A multi-objective feature selection approach for selecting key quality characteristics (KQCs) of unbalanced production data is proposed. We define KQC (feature) selection as a bi-objective problem of maximizing the quality characteristic (QC) subset importance and minimizing the QC subset size. Three candidate feature importance measures, the geometric mean (GM), F1 score and accuracy, are applied to construct three KQC selection models. To solve the models, a two-phase optimization method for selecting the candidate solutions (QC subsets) using a novel multi-objective optimization method (GADMS) and the final KQC set from the candidate solutions using the ideal point method (IPM) is proposed. GADMS is a hybrid method composed of a genetic algorithm (GA) and a local search strategy named direct multisearch (DMS). In GADMS, we combine binary encoding with real value encoding to utilize the advantages of GAs and DMS. The experimental results on four production datasets show that the proposed method with GM performs the best in handling the data imbalance problem and outperforms the benchmark methods. Moreover, GADMS obtains significantly better search performance than the benchmark multi-objective optimization methods, which include a modified nondominated sorting genetic algorithm II (NSGA-II), two multi-objective particle swarm optimization algorithms and an improved DMS method. |
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| AbstractList | •Three candidate bi-objective key quality characteristic selection models are proposed.•A new hybrid method of a GA and direct multisearch is proposed for optimization.•The model with geometric mean measure performs better than the other two models.•The proposed method effectively addresses the production data imbalance problem.•The optimization approach shows better search performance than benchmark methods.
A multi-objective feature selection approach for selecting key quality characteristics (KQCs) of unbalanced production data is proposed. We define KQC (feature) selection as a bi-objective problem of maximizing the quality characteristic (QC) subset importance and minimizing the QC subset size. Three candidate feature importance measures, the geometric mean (GM), F1 score and accuracy, are applied to construct three KQC selection models. To solve the models, a two-phase optimization method for selecting the candidate solutions (QC subsets) using a novel multi-objective optimization method (GADMS) and the final KQC set from the candidate solutions using the ideal point method (IPM) is proposed. GADMS is a hybrid method composed of a genetic algorithm (GA) and a local search strategy named direct multisearch (DMS). In GADMS, we combine binary encoding with real value encoding to utilize the advantages of GAs and DMS. The experimental results on four production datasets show that the proposed method with GM performs the best in handling the data imbalance problem and outperforms the benchmark methods. Moreover, GADMS obtains significantly better search performance than the benchmark multi-objective optimization methods, which include a modified nondominated sorting genetic algorithm II (NSGA-II), two multi-objective particle swarm optimization algorithms and an improved DMS method. |
| Author | Xue, Bing Zhang, Mengjie Li, An-Da |
| Author_xml | – sequence: 1 givenname: An-Da orcidid: 0000-0002-2111-8724 surname: Li fullname: Li, An-Da email: adli@tjcu.edu.cn organization: School of Management, Tianjin University of Commerce, Tianjin 300134, China – sequence: 2 givenname: Bing surname: Xue fullname: Xue, Bing organization: Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand – sequence: 3 givenname: Mengjie surname: Zhang fullname: Zhang, Mengjie organization: Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand |
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| Keywords | Key quality characteristics Feature selection Quality improvement Multi-objective optimization Unbalanced data |
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| SubjectTerms | Feature selection Key quality characteristics Multi-objective optimization Quality improvement Unbalanced data |
| Title | Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection |
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