Coefficient of Variation based Decision Tree (CvDT)
Hima Bindu K, Swarupa K Rani, Raghavendra Rao C · 2011
Abstract — Decision trees are widely used for classification. Several approaches exist to induce decision trees. All these methods vary in attribute selection measures i.e., in identifying an attribute to split at a node. This paper proposes a novel splitting criteria based on Coefficient of Variation and it is named as Coefficient of Variation Gain (CvGain). The decision trees built with CvGain are compared with those built with Entropy and Gainfix. Empirical analysis based on standard data sets revealed that Coefficient of Variation based decision tree (CvDT) has less computational cost and time.