Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method

An-Da Li, Zhen He, Qing Wang and Yang Zhang

European Journal of Operational Research, 2019, vol. 274, issue 3, 978-989

Abstract: The selection of key quality characteristics (KQCs) that are significantly associated with product quality is essential for improving product quality. Production lines generally yield a larger number of regular products than do premium products, which creates an imbalance in production datasets and complicates KQC selection. In this study, a KQC selection method with an excellent ability to predict product quality is proposed based on a two-phase bi-objective feature selection method. The KQC selection model is established as a bi-objective problem of maximizing feature (i.e., quality characteristic) importance and minimizing percentage of selected features, and the geometric mean (G-mean) is selected as the feature importance metric for imbalanced data. To solve this model, a two-phase multi-objective optimization method is proposed; this method yields a set of candidate solutions (KQC sets) using an improved direct multi-search (DMS) strategy and uses the ideal point method (IPM) to select the final KQC sets from the candidate solutions. The experimental results indicate that the proposed method is effective for selecting KQCs for imbalanced production data.

Keywords: Quality management; Direct multi-search (DMS); Feature selection; imbalanced data; Multi-objective optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:274:y:2019:i:3:p:978-989

DOI: 10.1016/j.ejor.2018.10.051

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