Abstract
In concept learning and data mining, a typical objective is to determine concept descriptions or patterns that will classify future data points as correctly as possible. If one can assume that the data contain no noise, then it is desirable that descriptions are complete and consistent with regard to all the data, i.e., they characterize all data points in a given class (positive examples) and no data points outside the class (negative examples).
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Michalski, R.S., Kaufman, K.A. (2001). Learning Patterns in Noisy Data: The AQ Approach. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_2
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DOI: https://doi.org/10.1007/3-540-44673-7_2
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