Comparison of Discriminant Analysis and Adaptive Boosting Classification and Regression Trees on Data with Unbalanced Class

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Abstract: This study aims to determine the best classification results among discriminant analysis, CART, and Adaboost CART on Bank X's Home Ownership Credit (KPR) customers. This study uses secondary data which contains notes on the 5C assessment (Collateral, Character, Capacity, Condition, Capital) and collectibility of current and non-current loans. The sample used in this study was from 2000 debtors. Comparison of classifications based on model accuracy, sensitivity, and overall specificity shows that Adaboost CART is the best method for classifying credit collectibility at Bank X. This is due to the class imbalance in the data. This study compares the classification results between parametric statistics, namely discriminant analysis and non-parametric statistics, namely CART and Adaboost CART. The results of the research can be used as material for consideration and evaluation for banks in determining the policy for providing credit to prospective borrowers from the classification results of KPR Bank X consumers.

WSEAS Transactions on Mathematics, ISSN / E-ISSN: 1109-2769 / 2224-2880, Volume 20, 2021, Art. #69

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Eva Fadilah Ramadhani, Adji Achmad Rinaldo Fernandes, Ni Wayan Surya Wardhani, "Comparison of Discriminant Analysis and Adaptive Boosting Classification and Regression Trees on Data with Unbalanced Class," WSEAS Transactions on Mathematics, vol. 20, pp. 650-656, 2021, DOI:10.37394/23206.2021.20.69
Eva Fadilah Ramadhani, Adji Achmad Rinaldo Fernandes, Ni Wayan Surya Wardhani. Comparison of Discriminant Analysis and Adaptive Boosting Classification and Regression Trees on Data with Unbalanced Class. WSEAS Transactions on Mathematics. 2021;20:650-656. 10.37394/23206.2021.20.69
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