An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data

TL;DR

This paper introduces an online learning algorithm for a neuro-fuzzy classifier that effectively handles mixed-attribute data, enabling real-time adaptation to streaming data without full retraining.

Contribution

It proposes an extended online learning algorithm for GFMMNN capable of processing both continuous and categorical features in real-time.

Findings

Superior classification accuracy compared to existing methods

Stable performance across diverse datasets

Effective handling of mixed-attribute streaming data

Abstract

General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data. While categorical features encoding methods can be used with the GFMMNN learning algorithms, they exhibit a lot of shortcomings. Other approaches proposed in the literature are not suitable for on-line learning as they require entire training data available in the learning phase. With the rapid change in the volume and velocity of streaming data in many application areas, it is increasingly required that the constructed models can learn and adapt to the continuous data changes in real-time without the need for their full retraining or access to the historical data. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms for the GFMM model.