An improved learning algorithm of decision tree based on entropy uncertainty deviation

Aimed at the problem of deviation of uncertainty estimates in the test model of attributes selecting with the information gain, an improved learning algorithm of decision tree based on the uncertainty deviation of entropy measure was developed. In the algorithm, the method of regulating oppositely deviation of the information entropy peak through a sine function was used, when test of attributes choice with information gain the adverse effect of deviation of information entropy peak was restrained. Compared with the ID3, the improvement of classification performance was acquired while its better stability of performance for its decision tree. The research results show that the rationality of attribute selection test was effectively improved through the method based on the entropy uncertainty deviation.