Fuzzifying Gini Index based decision trees

Crisp decision tree algorithms face the problem of having sharp decision boundaries which may not be found in all real life classification problems. A fuzzy decision tree algorithm Gini Index based (G-FDT) is proposed in this paper to fuzzify the decision boundary without converting the numeric attributes into fuzzy linguistic terms. Gini Index is used as split measure for choosing the most appropriate splitting attribute at each node. The performance of G-FDT algorithm is compared with Gini Index based crisp decision tree algorithm (SLIQ) using several real life datasets taken from the UCI machine learning repository. G-FDT algorithm outperforms its crisp counterpart in terms of classification accuracy. The size of the G-FDT is significantly less compared to SLIQ.