Incremental Ensemble Learning Model for Imbalanced Data: a Case Study of Credit Scoring

Abstract

Imbalanced data is a challenge for classification models. It reduces the overall performance of traditional learning algorithms. Besides, the minority class of imbalanced datasets is misclassified with a high ratio even though this is a crucial object of the classification process. In this paper, a new model called the Lasso-Logistic ensemble is proposed to deal with imbalanced data by utilizing two popular techniques, random over-sampling and random under-sampling. The model was applied to two real imbalanced credit data sets. The results show that the Lasso-Logistic ensemble model offers better performance than the single traditional methods, such as random over-sampling, random under-sampling, Synthetic Minority Oversampling Technique (SMOTE), and cost-sensitive learning.

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