Abstract
The Bayesian classification is a method based on the Bayes theorem which gives best result when attributes are independent of each other and data is normalized. In this paper, a two-step approach is proposed to classify the data attributes which are locally normalized but not globally. The first step involves finding value for each attribute where the gain ratio is maximum. The classification occurs in the second step on the two separate parts of data using the Bayesian classifier. The experimental results show that the accuracy of the proposed method is better than the Bayesian classification and Decision tree.
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Malik, A., Singh, A., Deshmukh, M. (2020). Classification of Imbalanced Data Using Decision Tree and Bayesian Classifier. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_43
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DOI: https://doi.org/10.1007/978-3-030-44289-7_43
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