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

Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification