TY - GEN
T1 - Credit risk classification using Kernel Logistic Regression with optimal parameter
AU - Rahayu, S. P.
AU - Zain, Jasni Mohammad
AU - Embong, A.
AU - Purnami, S. W.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78650287755&partnerID=8YFLogxK
U2 - 10.1109/ISSPA.2010.5605437
DO - 10.1109/ISSPA.2010.5605437
M3 - Conference contribution
AN - SCOPUS:78650287755
SN - 9781424471676
T3 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
SP - 602
EP - 605
BT - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
T2 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Y2 - 10 May 2010 through 13 May 2010
ER -