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Fuzzy If-Then Rules Classifier on Ensemble Data
- Source :
- Communications in Computer and Information Science ISBN: 9783662456514, ICMLC (CCIS volume)
- Publication Year :
- 2014
- Publisher :
- Springer Berlin Heidelberg, 2014.
-
Abstract
- This paper introduces a novel framework that uses fuzzy IF-THEN rules in an ensemble system. Our model tackles several drawbacks. First, IF-THEN rules approaches have problems with high dimensional data since computational cost is exponential. In our framework, rules are operated on outputs of base classifiers which frequently have lower dimensionality than the original data. Moreover, outputs of base classifiers are scaled within the range [0, 1] so it is convenient to apply fuzzy rules directly instead of requiring data transformation and normalization before generating fuzzy rules. The performance of this model was evaluated through experiments on 6 commonly used datasets from UCI Machine Learning Repository and compared with several state-of-art combining classifiers algorithms and fuzzy IF-THEN rules approaches. The results show that our framework can improve the classification accuracy.
- Subjects :
- Clustering high-dimensional data
Neuro-fuzzy
Computer science
business.industry
Machine learning
computer.software_genre
Fuzzy logic
Exponential function
Fuzzy electronics
ComputingMethodologies_PATTERNRECOGNITION
Data mining
Artificial intelligence
Intelligent control
business
computer
Classifier (UML)
Curse of dimensionality
Subjects
Details
- ISBN :
- 978-3-662-45651-4
- ISBNs :
- 9783662456514
- Database :
- OpenAIRE
- Journal :
- Communications in Computer and Information Science ISBN: 9783662456514, ICMLC (CCIS volume)
- Accession number :
- edsair.doi...........ce491edb4b21aa0f3535c83e1c985c9d