Abstract
Imbalanced data is widespread in the fields of medical diagnosis, information security and industrial production. Traditional classification methods can handle balanced data very well. However, when dealing with imbalanced classification, it will favor majority classes, which results in low classification performance. This paper proposes an imbalanced classification method based on deep feature representation, named DL-FSVM. DL-FSVM extracts feature information in the input space using a deep neural network (DNN) to ensure similarity within class and improve the separation between different classes. After obtaining the feature representation, oversampling is performed in this embedding space based on the center distance to enhance the balance of the data distribution. Fuzzy Support Vector Machine (FSVM) is used as the final classifier. Assigning higher misclassification costs to minority class samples through cost-sensitive learning. Experiments were performed on six real-world datasets. The experimental results show that DL-FSVM achieves promising classification performance in three evaluation metrics: G-means, F1-score and AUC.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61703279), in part by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100) and the Fundamental Research Funds for the Central Universities.
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Wang, K., An, J., Ma, X., Ma, C., Bao, H. (2022). Imbalance Classification Based on Deep Learning and Fuzzy Support Vector Machine. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_3
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