Abstract
In recent years, classification of imbalanced data get into trouble due to the imbalanced class distribution. Cost-Sensitive Learning (CSL) is one of the solutions at algorithm level, but this kind of method needs to provide cost information in advance. Mutual-Information Classification (MIC) is a Cost-Free Learning (CFL) method, which could summarize cost information automatically. But this method pays too much attention to minority class and ignores the accuracy of majority class data. Based on MIC, this paper proposed a CFL method for imbalanced data classification, called Mutual-Information-SMOTE Classification (MISC). Firstly, we uses mutual-information classifiers to generate the abstaining samples, which is difficult to be classified. Secondly, we use these abstention samples to synthetize new samples. Thirdly, we construct mutual-information-SMOTE classifiers using MIC and SMOTE. Finally, we uses our classifiers to obtain the final results. Numerical examples are given for indicating that MISC is more effective for imbalanced data classification through comparing with MIC.
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Acknowledgements
This work was supported by the National High Technology Research, Development Program of China (No. 2015IM03030), the National Natural Science Foundation of China (No. 61573235), the Shanghai Innovation Action Project of Science and Technology (No. 17511103502), and the Fundamental Research Funds for the Central Universities.
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Chen, Y., Chen, Y., Liu, X., Zhao, W. (2018). Mutual-Information-SMOTE: A Cost-Free Learning Method for Imbalanced Data Classification. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_2
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DOI: https://doi.org/10.1007/978-981-13-1648-7_2
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