Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection

Abstract 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.