@misc{Kubus_Mariusz_Porównanie_2009,
 author={Kubus, Mariusz},
 year={2009},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu = Research Papers of Wrocław University of Economics; 2009; Nr 47, s. 367-374},
 language={pol},
 abstract={Rules induction belongs to nonparametric and adaptive methods of discrimination. As in classification trees, it can deal with nonmetric variables and missing attribute values. The method is also robust in a presence of outliers. A model has the form of a set of "if-then" rules, where conditions are the conjunctions of attribute values, therefore it is easy for interpretation. Rules neither need be represented in the form of tree nor lead to disjoint classification. The main goal of this paper is the comparison of error rates for rules induction and some discrimination methods. Over twenty real world datasets from UCI Repository of Machine Learning Databases were used. The RIPPER algorithm, which is considered as one of the most effective in rules induction, has been chosen. (original abstract)},
 type={artykuł},
 title={Porównanie indukcji reguł z wybranymi metodami dyskryminacji},
}