Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company. A. Vieira Instituto Superior de Engenharia do Porto, Portugal B. Ribeiro University of Coimbra, Portugal A. Ribeiro Technical University of Lisbon, Portugal J. Neves Technical University of Lisbon, Portugal DOI: 10.4018/978-1-4666-1574-8.ch009

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