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Author Ragot, N. ♦ Anquetil, E.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2001
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Learning systems ♦ Decision trees ♦ Fuzzy systems ♦ Clustering algorithms ♦ Genetic algorithms ♦ Pattern recognition ♦ Inference algorithms ♦ Databases ♦ Fuzzy neural networks ♦ Radial basis function networks
Abstract This paper presents a new hybrid learning method for the construction of fuzzy decision trees. The main principle of this approach is to automatically generates a hierarchical organization of the knowledge coupled with local choice of the best feature subspace. To improve the representation, a double level of modeling is used. Firstly a pre-classification level searches fuzzy decision regions to operate a natural discrimination between classes. The second level refines the previous one, doing an intrinsic fuzzy modeling of the classes represented in the fuzzy regions. Moreover, the best feature subspace is determined locally by a genetic algorithm for each partitioning. Finally, to have an understandable and "transparent" representation, the fuzzy decision tree is formalized as a fuzzy inference system which is easily modifiable and can be optimized a posteriori. First experimental results conducted on classical benchmarks and on a handwritten digits database show the capacity of the hybrid learning approach to provide reliable and compact classification system.
Description Author affiliation: IRISA, Rennes, France (Ragot, N.; Anquetil, E.)
ISBN 078037293X
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2001-12-02
Publisher Place Australia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 444.30 kB
Page Count 4
Starting Page 1380
Ending Page 1383

Source: IEEE Xplore Digital Library