Multi-objective Optimisation-Based Feature Selection for Multi-label Classification

Abstract

In this short note we introduce multi-objective optimisation for feature subset selection in multi-label classification. We aim at optimise multiple multi-label loss functions simultaneously, using label powerset, binary relevance, classifier chains and calibrated label ranking as the multi-label learning methods, and decision trees and SVMs as base learners. Experiments on multi-label benchmark datasets show that the feature subset obtained through MOO performs reasonably better than the systems that make use of exhaustive feature sets.