# Improving Neural Network Classifier using Gradient-based Floating Centroid Method

## TL;DR

This paper introduces a gradient-based floating centroid (GDFC) method to improve neural network classifiers by addressing fixed-centroid issues, demonstrating better performance on benchmark datasets.

## Contribution

The paper proposes a novel gradient-based floating centroid method and a new loss function to enhance neural network classifier optimization.

## Key findings

- GDFC outperforms comparison methods on benchmark datasets.
- The new loss function improves classification accuracy.
- GDFC reduces computational complexity compared to evolutionary approaches.

## Abstract

Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.