A Novel Progressive Learning Technique for Multi-class Classification

TL;DR

This paper introduces a progressive learning method for multi-class classification that dynamically adapts to new classes without forgetting previous knowledge, suitable for real-time applications.

Contribution

It presents a novel neural network remodeling approach that automatically incorporates new classes while retaining prior knowledge, independent of class constraints.

Findings

The technique effectively learns new classes without performance degradation.

It outperforms existing methods on standard datasets.

The method is suitable for online, real-time learning environments.

Abstract

In this paper, a progressive learning technique for multi-class classification is proposed. This newly developed learning technique is independent of the number of class constraints and it can learn new classes while still retaining the knowledge of previous classes. Whenever a new class (non-native to the knowledge learnt thus far) is encountered, the neural network structure gets remodeled automatically by facilitating new neurons and interconnections, and the parameters are calculated in such a way that it retains the knowledge learnt thus far. This technique is suitable for real-world applications where the number of classes is often unknown and online learning from real-time data is required. The consistency and the complexity of the progressive learning technique are analyzed. Several standard datasets are used to evaluate the performance of the developed technique. A comparative study shows that the developed technique is superior.