Construction of Decision Tree : Attribute Selection Measures
R. Aruna · 2013
Attribute selection measure is a heuristic for selecting the splitting criterion that “best” separates a given data partition, D, of a class-labeled training tuples into individual classes. It determines how the tuples at a given node are to be split. The attribute selection measure provides a ranking for each attribute describing the given training tuples. The attribute having the best score for the measure is chosen as the splitting attribute for the given tuples.This paper, perform a comparative study of two attribute selection measures. The Information gain is used to select the splitting attribute in each node in the tree. The attribute with the highest information gain is chosen as the splitting attribute for the current node. The Gini index measures use binary split for each attribute. The attribute with the minimum gini index as selected as the splitting attribute. The results indicates that predicting a attribute selection in Gini index is more effective and simple compared to Information gain.