Multi-Label Classification Method Based on Extreme Learning Machines

TL;DR

This paper introduces a novel multi-label classification method using Extreme Learning Machines (ELM), demonstrating superior performance over existing techniques across various benchmark datasets from multiple domains.

Contribution

The paper presents a new ELM-based approach for multi-label classification and provides comprehensive comparative analysis with nine state-of-the-art methods.

Findings

Proposed method outperforms existing techniques on multiple datasets.

The approach is effective across multimedia, text, and biology domains.

Results show improved evaluation metrics compared to nine other methods.

Abstract

In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels. The traditional binary and multi-class classification problems are the subset of the multi-label problem with the number of labels corresponding to each sample limited to one. The proposed ELM based multi-label classification technique is evaluated with six different benchmark multi-label datasets from different domains such as multimedia, text and biology. A detailed comparison of the results is made by comparing the proposed method with the results from nine state of the arts techniques for five different evaluation metrics. The nine methods are chosen from different categories of multi-label methods. The comparative results shows that the proposed Extreme Learning Machine based multi-label classification technique is a better alternative than the existing state of the art methods for multi-label problems.