FOXANN: A Method for Boosting Neural Network Performance

TL;DR

FOXANN introduces a novel neural network model that replaces backpropagation with the Fox optimizer, achieving superior accuracy, lower loss, and better generalization on standard datasets compared to existing methods.

Contribution

This paper presents FOXANN, a new neural network training approach using the Fox optimizer, which enhances performance and interpretability over traditional algorithms.

Findings

FOXANN outperforms traditional ANN and logistic regression in accuracy.

FOXANN achieves a higher accuracy of 0.9969 on standard datasets.

FOXANN demonstrates lower validation loss, indicating better generalization.

Abstract

Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset samples are involved in both the training and validation stages. Moreover, the results show that FOXANN outperforms traditional ANN and logistic regression methods as well as other models proposed in the literature such as ABC-ANN, ABC-MNN, CROANN, and PSO-DNN, achieving a higher accuracy of 0.9969 and a lower validation loss of 0.0028. These results demonstrate that FOXANN is more effective than traditional methods and other proposed models across standard datasets. Thus, FOXANN effectively addresses the challenges in ML algorithms and improves classification performance.