Abstract
We present an inductive learning algorithm that allows for a partial completeness and consistence, i.e. that derives classification rules correctly describing, e.g, most of the examples belonging to a class and not describing most of the examples not belonging to this class. The problem is represented as a modification of the set covering problem that is solved by a greedy algorithm. The approach is illustrated on some medical data.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Kacprzyk, J., Szkatuła, G. (2005). An Inductive Learning Algorithm with a Partial Completeness and Consistence via a Modified Set Covering Problem. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_105
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DOI: https://doi.org/10.1007/11550907_105
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