Abstract

Recently, Machine Learning techniques have become very popular because of its effectiveness. This study, applies Kernel Logistic Regression (KLR) to the credit risk classification in an attempt to suggest a model with better classification accuracy. Credit risk classification is an interesting and important data mining problem in financial analysis domain. In this study, the optimal parameter values (regularization and kernel function) of KLR. are found by using a grid search technique with 5-fold cross-validation. Credit risk data sets from UCl machine learning are used in order to verify the effectiveness of the KLR method in classifying credit risk. The experiment results show that KLR has promising performance when compared with other Machine Learning techniques in previous research literatures.

Original languageEnglish
Title of host publication10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Pages602-605
Number of pages4
DOIs
Publication statusPublished - 2010
Event10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010 - Kuala Lumpur, Malaysia
Duration: 10 May 201013 May 2010

Publication series

Name10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010

Conference

Conference10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period10/05/1013/05/10

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