Heterogeneous node split measure for decision tree construction

A new heterogeneous node split measure (HSM) has been proposed in this paper for decision tree construction. The split measure HSM is derived from quasilinear mean of information gain. This helps in including proportionalities of class values from the sub-partitions and the entire dataset at the same time. This results in acquiring more information at the split point, which produces compact decision trees. Comparative performance evaluation of HSM on benchmark datasets with the well known node splitting measures Gini-index and Gain ratio shows that HSM is capable of generating decision trees which are lesser in height. The classification accuracy is also far superior and the computational time is also less using HSM as the split measure.