  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.
