This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.
|Original language||English (US)|
|Title of host publication||Rough Sets and Knowledge Technology|
|Number of pages||12|
|State||Published - 2013|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)