Multi-label Learning Through Minimum Spanning Tree-Based Subset Selection and Feature Extraction

Abstract

Extracting an effective feature set on the basis of dataset characteristics can be a useful proposition to address a classification task. For multi-label datasets, the positive and negative class memberships for the instance set vary from label to label. In such a scenario, a dedicated feature set for each label can serve better than a single feature set for all labels. In this article, we approach multi-label learning addressing the same concern and present our work Multi-label learning through Minimum Spanning Tree based subset selection and feature extraction (MuMST-FE). For each label, we estimate the positive and negative class shapes using respective Minimum Spanning Trees (MSTs), followed by subset selection based on the key lattices of the MSTs. We select a unique subset of instances for each label which participates in the feature extraction step. A distance based feature set is extracted for each label from the reduced instance set to facilitate final classification. The classifiers modelled from MuMST-FE is found to possess improved robustness and discerning capabilities which is established by the performance of the proposed schema against several state-of-the-art approaches on ten benchmark datasets.