Adaptive Decision Threshold-Based Extreme Learning Machine for Classifying Imbalanced Multi-label Data

Abstract

Multi-label learning is a popular area of machine learning research as it is widely applicable to many real-world scenarios. In comparison with traditional binary and multi-classification tasks, the multi-label data are more easily impacted or destroyed by an imbalanced data distribution. This paper describes an adaptive decision threshold-based extreme learning machine algorithm (ADT-ELM) that addresses the imbalanced multi-label data classification problem. Specifically, the macro and micro F-measure metrics are adopted as the optimization functions for ADT-ELM, and the particle swarm optimization algorithm is employed to determine the optimal decision threshold combination. We use the optimized thresholds to make decision for future multi-label instances. Twelve baseline multi-label data sets are used in a series of experiments o verify the effectiveness and superiority of the proposed algorithm. The experimental results indicate that the proposed ADT-ELM algorithm is significantly superior to many state-of-the-art multi-label imbalance learning algorithms, and it generally requires less training time than more sophisticated algorithms.