Abstract
Classification is an important data mining technique for classifying the items according to a series of associated features. Even so, most of them are not stable in performance, and they may get high classification accuracy rate in some datasets but poor in other issues. To solve this problem, in this paper, an integrated algorithm is proposed to keep balance between the classification accuracy rate and stability. The proposed algorithm integrates the K-Nearest Neighbor (KNN), Naive Bayes (NB), Regression Tree (RT), Random Forest (RF), Bagging, and Discriminant Analysis Classifier (DAC) using forecasting probability strategies. Specifically, the majority voting strategy and weighted voting strategy are presented using the forecasting probability obtained from the classification algorithms. To demonstrate the effectiveness of the proposed algorithm, numerous experiments are conducted by applying the classification algorithms to real mobile APP statistics. Results indicate that it can get a comprehensive and stable classification accuracy rate.
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Acknowledgement
This work is partially supported by The Natural Science Foundation of Guangdong Province (2018A030310575), and Research Foundation of Shenzhen University (85303/00000155).
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Cao, J., Wang, H., Pang, M. (2019). An Integrated Classification Algorithm Using Forecasting Probability Strategies for Mobile App Statistics. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_57
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