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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Bibliographic Details
Published in:Information sciences Vol. 523; pp. 245 - 265
Main Authors: Li, An-Da, Xue, Bing, Zhang, Mengjie
Format: Journal Article
Language:English
Published: Elsevier Inc 01.06.2020
Subjects:
Feature selection
Key quality characteristics
Multi-objective optimization
Quality improvement
Unbalanced data
Key quality characteristics
Feature selection
Quality improvement
Multi-objective optimization
Unbalanced data
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2020.03.032