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01.12.2011 | Focus

Fuzzy knowledge representation study for incremental learning in data streams and classification problems

verfasst von: Albert Orriols-Puig, Jorge Casillas

Erschienen in: Soft Computing | Ausgabe 12/2011

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Abstract

The extraction of models from data streams has become a hot topic in data mining due to the proliferation of problems in which data are made available online. This has led to the design of several systems that create data models online. A novel approach to online learning of data streams can be found in Fuzzy-UCS, a young Michigan-style fuzzy-classifier system that has recently demonstrated to be highly competitive in extracting classification models from complex domains. Despite the promising results reported for Fuzzy-UCS, there still remain some hot issues that need to be analyzed in detail. This paper carefully studies two key aspects in Fuzzy-UCS: the ability of the system to learn models from data streams where concepts change over time and the behavior of different fuzzy representations. Four fuzzy representations that move through the dimensions of flexibility and interpretability are included in the system. The behavior of the different representations on a problem with concept changes is studied and compared to other machine learning techniques prepared to deal with these types of problems. Thereafter, the comparison is extended to a large collection of real-world problems, and a close examination of which problem characteristics benefit or affect the different representations is conducted. The overall results show that Fuzzy-UCS can effectively deal with problems with concept changes and lead to different interesting conclusions on the particular behavior of each representation.

Vorheriger Artikel Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems

Nächster Artikel Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning

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We selected C4.5 instead of OnlineTree2 in the comparison on real-world problems with static concepts since C4.5 is specifically designed to deal with these types of problems and the algorithm code is available online.

Note the size of the window selected permits storing all the examples sampled for a specific concept; therefore, we would expect an optimal behavior of IBk after having seen 12,500 examples of the same concept.

http://sci2s.ugr.es/sicidm/multipleTest.zip.

F4 is introduced here for the first time, and its design is based on F3.

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Titel: Fuzzy knowledge representation study for incremental learning in data streams and classification problems
verfasst von: Albert Orriols-Puig
Jorge Casillas
Publikationsdatum: 01.12.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 12/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0668-x

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