Abstract: Against the decline of decision tree prediction accuracy rate for high-dimensional data with noise,this paper used the theory of noise-free principal component analysis(NFPCA) algorithm to improve C4.5 algorithm,forming the NFPCA-in-C4.5 algorithms.On one hand,the algorithm transforms the noise suppression problem into an optimization problem of a combination of fitting the data and controling the smoothness,getting the space of principal components.On the other hand,it lets the space of principal components back to the space of original data during the process of building a new node in the decision tree from the top to down,to avoid the loss of characteristic information permanently in the dimension reduction process.Experimental results show that NFPCA-in-C4.5 algorithm has effects of dimensionality reduction and noise reduction,and avoids significant reduction of prediction accuracy rate,which is caused by the loss of information and noise.

Key words: High-dimensional data with noise,Noise tolerance,Principal component analysis,C4.5 algorithm