The Comparison of Gini and Twoing Algorithms in Terms of Predictive Ability and Misclassification Cost in Data Mining: An Empirical Study

The classification tree is commonly used in data mining for investigating interaction among predictors, particularly. The splitting rule and the decision trees technique employ algorithms that are largely based on statistical and probability methods. Splitting procedure is the most important phase of classification tree training. The aim of this study is to compare Gini and Twoing splitting rules in terms of misclassification cost, obtained the optimal balanced trees and the importance of independent variables. This study shows that the results obtained using the Twoing criterion, as it yields a tree that is much more equally balanced than the tree obtained with the Gini criterion. Misclassification rate was slightly different for the two methods (19% using Twoing criterion and 21,2% for the Gini).Using Twoing splitting rule gets more importance level independent variables and the improvement values are higher than the Gini algorithm. All things being considered, the good performance of the Twoing splitting in this study combined with its robustness to get high classification accuracy, tree structure and the importance of independent variables.