Decision tree generation method and system thereof

Abstract

The invention provides a decision tree generation method and a system thereof. The method comprises the following steps of acquiring a training sample set and a sample attribute; calculating a type number of different values of each sample attribute in the training sample set; according to the type number, calculating a distinguishing degree of each sample attribute respectively; selecting the sample attribute with the highest distinguishing degree as a basis attribute of decision tree branch dividing; carrying out recursion calculation dividing on the divided training sample set according to the above method till that a branch node reaches a preset threshold so that decision tree generation is completed. The invention also provides the corresponding system. Through the method in the invention, selection of decision tree attribute dividing can be reasonably performed so that a dividing result is accurate and possesses a practical value.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and system for generating a decision tree. Background technique [0002] When the traditional decision tree divides branches, it uses the method of dividing based on the size of information entropy, and the results of division based on information entropy are usually poorly differentiated, resulting in the problem that it is impossible to judge which attributes are the main differences. attributes, resulting in poor reference for decision tree division results. Contents of the invention [0003] In order to solve the above problems, the present invention proposes a method and system for generating a decision tree. By calculating the discrimination degree of sample attributes and determining the division basis, the branch division of the decision tree can be made more reasonable and the decision tree has better reference. [0004] A method for generating a decision t...

AI Technical Summary

Problems solved by technology

[0002] When the traditional decision tree divides branches, it uses the method of dividing based on the size of information entropy, and the results of division based on information entropy are usually poorly differentiated, resulting in the problem that it is impossible to judge which attributes are the main differences. attributes, resulting in poor reference for decision tree division results

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