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All Journal International Journal of Electrical and Computer Engineering
Jovanne C. Alejandrino
University of Mindanao
Computer Science & IT Electrical & Electronics Engineering

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Supervised and unsupervised data mining approaches in loan default prediction Jovanne C. Alejandrino; Jovito Jr. P. Bolacoy; John Vianne B. Murcia
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1837-1847

Abstract

Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k -NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
Co-Authors John Vianne B. Murcia Jovito Jr. P. Bolacoy