Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification

TL;DR

This paper introduces a novel data normalization method using eigenvalues from PCA to enhance neural network classification performance, demonstrating significant empirical improvements across multiple datasets.

Contribution

It proposes a new data preprocessing technique that weights PCA components by eigenvalues, improving ANN performance beyond traditional normalization methods.

Findings

Significant accuracy improvements in classification tasks

Effective in various datasets and problem types

Theoretically justified and empirically validated

Abstract

Artificial neural network (ANN) is a very useful tool in solving learning problems. Boosting the performances of ANN can be mainly concluded from two aspects: optimizing the architecture of ANN and normalizing the raw data for ANN. In this paper, a novel method which improves the effects of ANN by preprocessing the raw data is proposed. It totally leverages the fact that different features should play different roles. The raw data set is firstly preprocessed by principle component analysis (PCA), and then its principle components are weighted by their corresponding eigenvalues. Several aspects of analysis are carried out to analyze its theory and the applicable occasions. Three classification problems are launched by an active learning algorithm to verify the proposed method. From the empirical results, conclusion comes to the fact that the proposed method can significantly improve the performance of ANN.