An improved fuzzy classification system for financial credit decision using multi-objective evolutionary optimization

Abstract

In fuzzy classification system, accuracy has been gained at the cost of interpretability and vice versa. This situation is known as Interpretability-Accuracy Trade-off. To handle this trade-off between accuracy and interpretability the evolutionary algorithms (EAs) are often used to optimize the performance of the fuzzy classification system. From the last two decades, several multi-objective evolutionary systems have been designed and successfully implemented in several fields for finding multiple solutions at a single run. In Financial Decision making concerning Credit Allocation, Classification is a significant component to obtain credit scores and predict bankruptcy. A fuzzy classification system for the financial credit decision has been designed and find out the Accuracy and Interpretability parameters for applying various MOEAs to get the pareto optimal solution resulting in to improvement in the performance of the proposed system. The proposed model implemented on standard benchmark financial credit allocation datasets i.e., German Credit Approval system available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml) and using the open source tool MOEA framework (http://www.moeaframework.org). The experimental analysis highlights that the NSGA-III works efficiently for financial credit approval system and improves the performance by making a balanced trade-off between accuracy and interpretability.