Abstract
Classification of imbalanced datasets is one of the challenges in machine learning and data mining domains. The traditional classifiers still need to handle with minority instances. In this paper, we propose an effective method which applies sampling method based on ensemble learning. It uses Adaboost-SVM based on spectral clustering to boost the performance. This method also uses over-sampling and under-sampling methods based on the misclassified instances got by ensemble learning. Compared with the preview algorithms, the experiment results show that the proposed method is effective in dealing with imbalanced data in binary classification.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China (61273225, 61273303, 61373109), the Program for Outstanding Young Science and Technology Innovation Teams in Higher Education Institutions of Hubei Province (No. T201202), and the Program of Wuhan Subject Chief Scientist (201150530152), as well as National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAC22B01).
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Zhang, C., Zhang, X. (2017). An Effective Sampling Strategy for Ensemble Learning with Imbalanced Data. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_33
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DOI: https://doi.org/10.1007/978-3-319-63315-2_33
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