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All Journal Xplore: Journal of Statistics
Akmala Firdausi
Department of Statistics, IPB University
Decision Sciences, Operations Research & Management Engineering Mathematics

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Kajian Metode Pohon Model Logistik (Logistic Model Tree) dengan Penanganan Ketakseimbangan Data Akmala Firdausi; Aam Alamudi; Kusman Sadik
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.431 KB) | DOI: 10.29244/xplore.v11i2.922

Abstract

Logistic model tree is a nonparametric modelling method that combines decision tree with linear logistic regression. Logistic model tree handles multicollinearity well, but is not immune to problems that arise due to data imbalance. This study was carried to compare the performance of undersampling, SMOTE, and ROSE in handling imbalanced data when used in tandem with logistic model tree. The data used in the simulation was obtained by generating random numbers following the Bernoulli distribution as the response variable and the Bivariate Normal distribution as the explanatory variables, based on five different imbalance levels. Comparisons done on the AUC value showed that logistic model trees built with methods to handle imbalanced data performed better than logistic model trees built without applying any such method on every level of tested data imbalance in classifying objects. Among those, logistic model trees built with ROSE performed better than logistic model trees built with other methods. On datasets with low level of imbalance, the performance of logistic model trees built with ROSE and undersampling do not significantly differ.
Co-Authors Aam Alamudi Kusman Sadik