A New Splitting Criterion of Decision Trees
Xingyi Liu · Computer Technology and Development · 2008
Classification is an important issue on data mining and machine learning.Selecting splitting attributes is the key process during constructing decision tree for receiving the maximized classification accuracy.Existing methods for classification usually can be the method based on entropy,GINI index,and so on.Analyses the disadvantages and the advantages of the method which is utilized to select splitting attributes based on information gain theory,and proposes a statistical method which employs chi-squared test to get the relation between the condition attributes and the class label.Demonstrate experimental this algorithm and the results show this method is significantly well than the methods based on information theory.