The underlying idea for learning strategies processing missing attribute values relies on the class distribution; i.e., the class-sensitive frequencies are utilized. As soon as we substitute a missing value by a suitable one, we take the desired class of the example into consideration in order not to increase the noise in the data set. On the other hand, the overall (class-independent) frequencies are applied within classification.
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(2017). Overall and Class-Sensitive Frequencies. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_622
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