Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification
This is an incremental improvement for researchers and practitioners using ANNs in classification, focusing on data normalization to enhance model accuracy.
The paper tackles the problem of improving artificial neural network (ANN) classification performance by proposing a novel data preprocessing method that weights principal components based on eigenvalues, and the result shows it significantly boosts ANN performance across three classification tasks.
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.