Comparison of Decision Trees with Rényi and Tsallis Entropy Applied for Imbalanced Churn Dataset
Krzysztof Gajowniczek, Tomasz Ząbkowski, Arkadiusz Orłowski
Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 39–44 (2015)
Abstract. Two algorithms for building classification trees, based on Tsallis and Renyi entropy, are proposed and applied to customer churn problem. The dataset for modeling represents highly unbalanced proportion of two classes, which is often found in real world applications, and may cause negative effects on classification performance of the algorithms. The quality measures for obtained trees are compared for different values of α parameter.