A Heuristic Approach to Learning Rules from Fuzzy Databases

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...

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Bibliographic Details
Published inIEEE intelligent systems Vol. 22; no. 2; pp. 62 - 68
Main Authors Ranilla, J., Rodriguez-Muniz, L.J.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.03.2007
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
and probabilistic reasoning
Applied sciences
Artificial intelligence
Classification tree analysis
Computer science; control theory; systems
Crisps
Data mining
Decision trees
Entropy
Exact sciences and technology
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Gain measurement
Heuristic
Impurities
knowledge acquisition
Learning
machine learning
Probability
rule-based processing
uncertainty
Working environment noise
Fuzzy logic
Cognitive theory
Uncertain system
Classification
Database
Heuristic approach
Artificial intelligence
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Summary: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
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ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2007.19