Abstract
In the paper, two families of lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on ordinary and inhibitory rules, but the direct generation of rules is not required. Instead of this, the considered algorithms extract efficiently for a new object some information on the set of rules which is next used by a decision-making procedure.
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Delimata, P., Moshkov, M., Skowron, A., Suraj, Z. (2007). Two Families of Classification Algorithms. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_35
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DOI: https://doi.org/10.1007/978-3-540-72530-5_35
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