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Ensemble method based on individual evolving classifiers
- Source :
- EAIS, e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid, instname
- Publication Year :
- 2013
- Publisher :
- IEEE, 2013.
-
Abstract
- Humans often seek a second or third opinion about an important matter. Then, a final decision is reached after weighing and combining these opinions. This idea is the base of the ensemble based systems. Ensembles of classifiers are well established as a method for obtaining highly accurate classifiers by combining less accurate ones. On the other hand, evolving classifiers are inspired by the idea of evolve their structure in order to adapt to the changes of the environment. In this paper, we present a proof-of-concept method for constructing an ensemble system based on Evolving Fuzzy Systems. The main contribution of this approach is that the base-classifiers are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Thus, we present an ensemble system which is based on evolving classifiers and keeps the properties of the evolving approach classification of streaming data. It is important to clarify that the evolving classifiers are gradually developing but they are not genetic or evolutionary. This work has been supported by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03-03).
- Subjects :
- Informática
Structure (mathematical logic)
Training data
Computer science
business.industry
Fuzzy set
Conferences
Fuzzy control system
Base (topology)
Machine learning
computer.software_genre
Adaptive systems
Boosting
Random subspace method
ComputingMethodologies_PATTERNRECOGNITION
Ensembles of classifiers
Bagging
Streaming data
Intelligent systems
Training
Artificial intelligence
business
computer
Cascading classifiers
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)
- Accession number :
- edsair.doi.dedup.....df6aef5cb7f6b9a9a3aff08eac676b0b