Abstract
Based on the principle of cost sensitivity, this paper takes the cost sensitivity algorithm of neural network as the classifier algorithm, by using the idea of iteration, we can find a misclassification cost which can make the misclassification number of minority class samples to be zero. And compared with the evaluation indexes of some unbalanced data classification methods when the number of classification errors of minority class is non-zero, this paper hopes to realize the assumption that the minority class in the unbalanced data set will not be misclassified.
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Wang, Y., Wang, N. (2020). Study on an Extreme Classification of Cost - Sensitive Classification Algorithm. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2019. Advances in Intelligent Systems and Computing, vol 1117. Springer, Singapore. https://doi.org/10.1007/978-981-15-2568-1_250
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DOI: https://doi.org/10.1007/978-981-15-2568-1_250
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