Development of an Evolutionary Cost Sensitive Decision Tree Induction Algorithm
Mohammad Kassim, Sunil Vadera · 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) · 2022
This paper develops an Evolutionary Elliptical Cost-Sensitive Decision Tree Algorithm (EECSDT) which learns cost-sensitive non-linear decision trees for multiclass problems. EECSDT is developed by formulating the problem as an optimization task in which the objective is to minimize classification cost and where elliptical decision boundaries are adopted instead of axis parallel boundaries. EECSDT is implemented using MOEA, a framework for multi-objective evolutionary algorithms, and evaluated on fourteen data sets. An empirical evaluation with J48, NBTree, MetaCost, and the CostSensitiveClassifier in Weka shows that EECSDT performs better on 11 out of the 14 data sets in terms of accuracy, and 10 out of the 14 data sets in terms of minimizing cost. It also produces smaller trees on 8 out of the 11 datasets for which it achieves higher accuracy than use of axis parallel boundaries.