Abstract
The predictive accuracy of classifiers is determined among others by the quality of data. This important property of data is strongly affected by such factors as the number of erroneous or missing attributes present in the dataset. In this paper we show how those factors can be handled by introducing the levels of abstraction in data definition. Our approach is especially valuable in cases where giving the precise value of an attribute is impossible for a number of reasons as for example lack of time or knowledge. Furthermore, we show that increasing the level of precision for an attribute significantly increase predictive accuracy, especially when it is done for the attribute with high information gain.
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Łukaszewski, T., Józefowska, J., Ławrynowicz, A., Józefowski, Ł., Lisiecki, A. (2011). Controlling the Prediction Accuracy by Adjusting the Abstraction Levels. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_37
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DOI: https://doi.org/10.1007/978-3-642-21219-2_37
Publisher Name: Springer, Berlin, Heidelberg
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