Abstract
In real world settings there is situation where class distribution of data may change after classifier is built resulting in performance degradation of classifier. Attempts to solve this problem from previous Class Distribution Estimation method (CDE method) yield quite interesting performance however we notice there is some flaw since CDE method still have some bias toward train data thus we decide to improve them with ensemble method. Our Class Distribution Estimation-Ensemble (CDE-EM) methods estimate class distribution from many models instead of one resulting in less bias than previous method. All methods are evaluated using accuracy on set of benchmark UCI data sets. Experimental results demonstrate that our methods yield better performance if class distribution of test data is different from train data.
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Limsetto, N., Waiyamai, K. (2011). Handling Concept Drift via Ensemble and Class Distribution Estimation Technique. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_2
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DOI: https://doi.org/10.1007/978-3-642-25856-5_2
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