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Credit risk evaluation modeling using evolutionary linear SVM classifiers and sliding window approach

31 citations0/0 files storedProcedia Computer Science

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

This paper presents a study on credit risk evaluation modeling using linear Support Vector Machines (SVM) classifiers, combined with evolutionary parameter selection using Genetic Algorithms and Particle Swarm Optimization, and sliding window approach. Discriminant analysis was applied for evaluation of financial instances and dynamic formation of bankruptcy classes. The possibilities of feature selection application were also researched by applying correlation-based feature subset evaluator. The research demonstrates a possibility to develop and apply an intelligent classifier based on original discriminant analysis method evaluation and shows that it might perform bankruptcy identification better than original model.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 2006A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy 2008Hamilton, New Zealand 2011Credit Risk Evaluation Model Development Using Support Vector Based Classifiers 2006A Hybrid SOM-Altman Model for Bankruptcy Prediction

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2022Predicting earnings management through machine learning ensemble classifiers 2022Analysis of clustering algorithms for credit risk evaluation using multiple correspondence analysis

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