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Mediana Aryuni,Evaristus Didik Madyatmadja 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.5
The performance of credit scoring models is determined by the used features. The relevant features for credit scoring usually are determined unsystematic and dominate by arbitrary trial. This paper presents a comparative study of four feature selection methods, which use data mining approach in reducing the feature space. The final results show that among the four feature selection methods, the Gini Index and Information Gain algorithms perform better than others with the classification accuracy of 75.46% and 75.44% respectively.
Mediana Aryuni,Suko Adiarto,Eka Miranda,Evaristus Didik Madyatmadja,Albert Verasius Dian Sano,Elvin Sestomi 한국지능시스템학회 2023 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.23 No.2
In the field of medical data mining, imbalanced data categorization occurs frequently, whichtypically leads to classifiers with low predictive accuracy for the minority class. This studyaims to construct a classifier model for imbalanced data using the SMOTE oversamplingalgorithm and a heart disease dataset obtained from Harapan Kita Hospital. The categorizationmodel utilized logistic regression, decision tree, random forest, bagging logistic regression,and bagging decision tree. SMOTE improved the model prediction accuracy with imbalanceddata, particularly for minority classes