Title: Genetic programming-based evolution of classification trees for decision support in banking sector

Authors: Radhika Kotecha; Sanjay Garg

Addresses: Computer Engineering Department, Institute of Technology, Nirma University, Ahmedabad, India ' Computer Engineering Department, Institute of Technology, Nirma University, Ahmedabad, India

Abstract: Credit decision-making is a vital process in the banking sector as it helps to reduce losses by identifying non-creditable individuals. Classification algorithms in data mining provide accurate results in the aforementioned area. But, such real-world lending environments require classification results to be easy to interpret. The lack of explicability of several existing classifiers makes banks reluctant in using them. An ideal classifier needs to be accurate with interpretability encapsulated within it. Decision trees are accurate, but for large datasets, the tree becomes very large and may not be comprehensible. Genetic programming (GP) is widely applied for solving classification problems since it can produce smaller trees by using tree-size, as fitness measure or by depth-limiting the trees. Hence, we propose an algorithm named GPeCT that merges decision tree and GP to produce a near-optimal decision tree classifier. We demonstrate the performance of GPeCT through experiments on large datasets from banking and other domains.

Keywords: decision-making; data mining; classification; decision trees; evolutionary algorithms; genetic programming; GP.

DOI: 10.1504/IJKESDP.2016.084599

International Journal of Knowledge Engineering and Soft Data Paradigms, 2016 Vol.5 No.3/4, pp.186 - 204

Received: 20 Jun 2016
Accepted: 28 Oct 2016
Published online: 17 Jun 2017 *

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