Automatic Construction of Pattern Classifiers Capable of Continuous Incremental Learning and Unlearning Tasks Based on Compact-Sized Probabilistic Neural Network

TL;DR

This paper introduces a compact probabilistic neural network that can learn and unlearn classes incrementally without hyperparameter tuning, maintaining high classification accuracy with fewer hidden units.

Contribution

It presents a simple, data-driven network-growing algorithm enabling continuous incremental learning and unlearning without iterative matrix computations.

Findings

Achieves similar performance to multilayer perceptrons

Uses fewer hidden units for compactness

Effectively handles incremental and unlearning tasks

Abstract

This paper proposes a novel approach to pattern classification using a probabilistic neural network model. The strategy is based on a compact-sized probabilistic neural network capable of continuous incremental learning and unlearning tasks. The network is constructed/reconstructed using a simple, one-pass network-growing algorithm with no hyperparameter tuning. Then, given the training dataset, its structure and parameters are automatically determined and can be dynamically varied in continual incremental and decremental learning situations. The algorithm proposed in this work involves no iterative or arduous matrix-based parameter approximations but a simple data-driven updating scheme. Simulation results using nine publicly available databases demonstrate the effectiveness of this approach, showing that compact-sized probabilistic neural networks constructed have a much smaller number of hidden units compared to the original probabilistic neural network model and yet can achieve a similar classification performance to that of multilayer perceptron neural networks in standard classification tasks, while also exhibiting sufficient capability in continuous class incremental learning and unlearning tasks.