Abstract
The accuracy of the rules produced by a concept learning system can be hindered by the presence of errors in the data. Although these errors are most commonly attributed to random noise, there also exist “ill-defined” attributes that are too general or too specific that can produce systematic classification errors. We present a computer program called Newton which uses the fact that ill-defined attributes create an ordered error pattern among the instances to compute hypotheses explaining the classification errors of a concept in terms of too general or too specific attributes. Extensive empirical testing shows that Newton identifies such attributes with a prediction rate over 95%.
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Hallé, S. (2005). Using Boolean Differences for Discovering Ill-Defined Attributes in Propositional Machine Learning. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_43
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DOI: https://doi.org/10.1007/11579427_43
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