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Autor
Piegat Andrzej (West Pomeranian University of Technology in Szczecin), Landowski Marek (Maritime University of Szczecin)
Tytuł
Specialized, MSE-Optimal m-Estimators of the Rule Probability Especially Suitable for Machine Learning
Źródło
Control and Cybernetics, 2014, vol. 43, nr 1, s. 133-160, rys., tab., bibliogr. s. 158-160
Słowa kluczowe
Uczenie maszynowe, Data Mining, Drzewo decyzyjne
Machine learning, Data Mining, Decision tree
Uwagi
summ.
Abstrakt
The paper presents an improved sample based rule- probability estimation that is an important indicator of the rule quality and credibility in systems of machine learning. It concerns rules obtained, e.g., with the use of decision trees and rough set theory. Particular rules are frequently supported only by a small or very small number of data pieces. The rule probability is mostly investigated with the use of global estimators such as the frequency-, the Laplace-, or the m-estimator constructed for the full probability interval [0,1]. The paper shows that precision of the rule probability estimation can be considerably increased by the use of m-estimators which are specialized for the interval [phmin, phmax] given by the problem expert. The paper also presents a new interpretation of the m-estimator parameters that can be optimized in the estimators. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
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Bibliografia
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ISSN
0324-8569
Język
eng
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