A Decision-Tree Classifier Model of Competition in Choosing Split Attribute
Zeyu Huang · Computer Technology and Development · 2006
The construction of decision-tree is centered on the selection algorithm of an attribute that generates a partition of the subsets of the training database that is located in the node about to be split.On the basis of analyzing three techniques for choosing the splitting attributes including the entropy gain and the gain ratio,the gini index and Goodman-Kruskal association index,propose a strategy to improve on classical decision-tree classifier C4.5 arithmetic(a decision-tree classifier model of competition in choosing split attribute).Experimental results show it is possible,in most cases,to obtain smaller decision trees without sacrificing accuracy.