A multi-stage multi-objective GWO based feature selection approach for multi-label text classification

In Information Retrieval (IR), Text Mining (TM), and web search, Multi-label Text Classification (MTC) plays an essential role. A document can fall into more than one category in MTC. Text documents frequently include High Dimensional (HD) non-discriminative (noisy and irrelevant) phrases, resulting in high computing costs and impoverish learning performance of Text Classification (TC). The Feature Selection (FS) procedure is complicated by three issues caused by small samples and HD datasets. First, given limited samples and HD, FS is unstable. Second, with HD, FS takes longer. Third, a particular FS approach may not provide enough Classification Accuracy (CA). In this paper, we have developed a two-stage FS approach based Meta-heuristics Algorithm (MA) for MTC. The first stage work on the filter-based FS approach, while the second stage is based on the multi-objective Grey Wolf Optimization (GWO) algorithm. The first objective is to diminish the Hamming Loss (HL), and the second objective is to decrease the Selected Features (SF). We have used the Multi-Layer Perceptron (MLP) model for the classification task. The experimental findings show that the suggested FS scheme achieves superior HL with a less number of features.