Abstract
Learning from imbalanced datasets is a challenge in machine learning, oversampling is an effective method to solve the problem of class imbalance, owing to its easy-to-go capability of achieving the balance by synthesizing new samples. However several problems still exist such as noise samples, selection of boundary samples and the diversity of synthetic samples. To solve these problems, this paper proposes a new improved oversampling method based on SMOTE and genetic algorithm (GA-SMOTE). The main steps of GA-SMOTE are as follows. Firstly GA-SMOTE uses genetic algorithm to find an optimal noise processing scheme. Then GA-SMOTE assigns different sampling weight to each sample and the sample closer to the boundary is assigned greater weight. Finally, GA-SMOTE divides raw dataset into multiple sub-clusters by K-means clustering and intra-cluster neighborhood triangular sampling method is used in each sub-cluster to improve the diversity of synthetic samples. A large number of experiments have proved that GA-SMOTE is superior to the other five comparison methods in dealing with imbalanced data classification.
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Gong, J. (2021). A Novel Oversampling Technique for Imbalanced Learning Based on SMOTE and Genetic Algorithm. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_17
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