- Tytuł:
- Specialized, MSE-optimal m-estimators of the rule probability especially suitable for machine learning
- Autorzy:
-
Piegat, A.
Landowski, M. - Powiązania:
- https://bibliotekanauki.pl/articles/205508.pdf
- Data publikacji:
- 2014
- Wydawca:
- Polska Akademia Nauk. Instytut Badań Systemowych PAN
- Tematy:
-
machine learning
rule probability
probability estimation
m-estimators
decision trees
rough set theory - Opis:
- 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.
- Źródło:
-
Control and Cybernetics; 2014, 43, 1; 133-160
0324-8569 - Pojawia się w:
- Control and Cybernetics
- Dostawca treści:
- Biblioteka Nauki
Artykuł