Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network

TL;DR

This paper presents a novel binary classifier called Chi-Square Test Neural Network that utilizes chi-square test principles to redefine cost and error functions, improving data distribution consistency and classification accuracy.

Contribution

It introduces a new neural network model that integrates chi-square test theory into the backpropagation framework for enhanced binary classification.

Findings

Significantly improved classification accuracy on real-world datasets

Ensures consistent data distribution between training and testing sets

Demonstrates advantages over existing related approaches

Abstract

We introduce the chi-square test neural network: a single hidden layer backpropagation neural network using chi-square test theorem to redefine the cost function and the error function. The weights and thresholds are modified using standard backpropagation algorithm. The proposed approach has the advantage of making consistent data distribution over training and testing sets. It can be used for binary classification. The experimental results on real world data sets indicate that the proposed algorithm can significantly improve the classification accuracy comparing to related approaches.