Abstract
The characteristics of the minority class distribution in imbalanced data is studied. Four types of minority examples – safe, borderline, rare and outlier – are distinguished and analysed. We propose a new method for identification of these examples in the data, based on analysing the local neighbourhoods of examples. Its application to UCI imbalanced datasets shows that the minority class is often scattered without too many safe examples. This characteristics of data distributions is also confirmed by another analysis with Multidimensional Scaling visualization. We examine the influence of these types of examples on 6 different classifiers learned over various real-world datasets. Results of experiments show that the particular classifiers reveal different sensitivity to the type of examples.
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Napierala, K., Stefanowski, J. (2012). Identification of Different Types of Minority Class Examples in Imbalanced Data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_14
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DOI: https://doi.org/10.1007/978-3-642-28931-6_14
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