A Heuristic Approach to Learning Rules from Fuzzy Databases

Author

Ranilla, José ; Rodríguez-Muñiz, Luis J.

Author_Institution

Oviedo Univ.

Volume

22

Issue

2

fYear

2007

Firstpage

62

Lastpage

68

Abstract

As an alternative to approaches based on entropy and information gain, we describe a system that uses a measure called the impurity level. The learning algorithm based on this measure, which we call FARNI, first induces fuzzy decision trees by using an impurity-level extension for selecting the best branch. This is similar to the way C4.5 and ARNI induce selections for crisp databases. Once FARNI calculates the fuzzy decision tree, it returns compact fuzzy rule sets that apply a pruning process

Keywords

data mining; database management systems; decision trees; fuzzy set theory; learning (artificial intelligence); FARNI impurity level measure; data pruning process; fuzzy database; fuzzy decision tree; fuzzy rule set; rule learning algorithm; Classification tree analysis; Databases; Decision trees; Fuzzy logic; Fuzzy sets; Fuzzy systems; Gain measurement; Impurities; Probability; Working environment noise; and probabilistic reasoning; fuzzy sets; knowledge acquisition; machine learning; rule-based processing; uncertainty;

fLanguage

English

Journal_Title

Intelligent Systems, IEEE

Publisher

ieee

ISSN

1541-1672

Type

jour

DOI

10.1109/MIS.2007.19

Filename

4136861