Data Centroid Based Multi-Level Fuzzy Min-Max Neural Network

TL;DR

This paper introduces an extension to multi-level fuzzy min-max neural networks that utilizes data centroids to improve classification accuracy, especially in ambiguous boundary regions, demonstrated through standard datasets.

Contribution

The paper proposes a novel data centroid-based approach to enhance multi-level fuzzy min-max neural networks for more accurate pattern classification.

Findings

Improved classification accuracy on standard datasets.

Enhanced decision confidence in boundary regions.

Consistent performance gains over previous methods.

Abstract

Recently, a multi-level fuzzy min max neural network (MLF) was proposed, which improves the classification accuracy by handling an overlapped region (area of confusion) with the help of a tree structure. In this brief, an extension of MLF is proposed which defines a new boundary region, where the previously proposed methods mark decisions with less confidence and hence misclassification is more frequent. A methodology to classify patterns more accurately is presented. Our work enhances the testing procedure by means of data centroids. We exhibit an illustrative example, clearly highlighting the advantage of our approach. Results on standard datasets are also presented to evidentially prove a consistent improvement in the classification rate.