A Novel Progressive Multi-label Classifier for Classincremental Data

TL;DR

This paper introduces a novel progressive multi-label classifier based on Extreme Learning Machine that incrementally learns new labels while preserving previous knowledge, suitable for streaming data in real-world applications.

Contribution

It is the first multi-label classifier designed for class-incremental learning, automatically restructuring neural network connections as new labels are introduced.

Findings

Effective on synthetic and real datasets

Outperforms existing methods in incremental learning scenarios

Demonstrates efficiency and robustness

Abstract

In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added and the neural network connections and parameters are automatically restructured as if the label has been introduced from the beginning. This work is the first of the kind in multi-label classifier for class-incremental learning. It is useful for real-world applications such as robotics where streaming data are available and the number of labels is often unknown. Based on the Extreme Learning Machine framework, a novel universal classifier with plug and play capabilities for progressive multi-label classification is developed. Experimental results on various benchmark synthetic and real datasets validate the efficiency and effectiveness of our proposed algorithm.