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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Empirical Analysis of Preprocessing Techniques for Imbalanced Dataset Using Logistic Regression
verfasst von : M. Revathi, D. Ramyachitra
Erschienen in: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering
Verlag: Springer Nature Singapore
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Abstract
This paper attempts to examine the performance of preprocessing strategies with logistic regression classifier. The goal of this paper is to see if there is a feasible and efficient strategy to enhance the performance of classification techniques on imbalanced datasets for different training dataset percentages. The experiments were conducted on Cleveland dataset—binary class. Several data preprocessing methods like Smote, Borderline-Smote, and ADAYSN were applied to data in order to classify various training dataset percentages. It was necessary to ascertain how the training dataset percentage affected the final classification for preprocessing methods. The experimental results explained that the ratio of 70–30 datasets performed better or better than other ratios when on train and test datasets, respectively. It was found from experimental results that the algorithms gave better accuracy when the training to testing ratio was 70:30 compared to other ratios.