Abstract
Imbalance data is in important topic vexed researchers in practice of classification problems. A data is imbalanced if the distributions of categories are not approximately equally represented. The class with small samples is called minority class, while the other classes form the majority class. Standard learning classifiers tend to misclassify the minority samples; they assume that the distribution of data is relatively balanced. However in real world application, the corrected prediction of minority samples is more valuable than correctly classify samples belonging to the majority class. In this paper, we propose GNOT a novel oversampling strategy that combines algorithm genetic concept and novelty detection technique to generate consistent with the original distribution of the minority class while avoiding outliers. We tested GNOT on seven real-world imbalanced datasets. Our experimental analysis shows that GNOT can effectively improve the performance of classifiers in terms of G-mean and F1-measure.
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Ait Addi, H., Ezzahir, R., Boukhlik, N. (2023). Genetic-Novelty Oversampling Technique for Imbalanced Data. In: Lazaar, M., En-Naimi, E.M., Zouhair, A., Al Achhab, M., Mahboub, O. (eds) Proceedings of the 6th International Conference on Big Data and Internet of Things. BDIoT 2022. Lecture Notes in Networks and Systems, vol 625. Springer, Cham. https://doi.org/10.1007/978-3-031-28387-1_16
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