24 September 2001 New algorithm for combining classifiers based on fuzzy integral and genetic algorithms
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Proceedings Volume 4554, Object Detection, Classification, and Tracking Technologies; (2001); doi: 10.1117/12.441640
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China
Combination of many different classifiers can improve classification accuracy. Sugeno and choquet integrals with respect to the fuzzy measure possess many desired properties, so in this paper they are used to combine multiple neural network classifiers. However, it is difficult to determine fuzzy measures in real problems. In this paper, we present two methods, one is that we assign the degree of importance of each network based on how good these networks classify each class of the training data, the other is by genetic algorithms (GAs), to obtain fuzzy measures, each taking into account the intuitive idea that each classifier always possesses different classification ability for each class. In the experiment, several databases in UCI repository are tested using these combination schemes and compared with C4.5. They are also applied to a multisensor fusion system for workpiece identification. Experimental results confirm the superiority of these presented methods.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yurong Li, Jingping Jiang, "New algorithm for combining classifiers based on fuzzy integral and genetic algorithms", Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441640; https://doi.org/10.1117/12.441640

Fuzzy logic

Neural networks

Atomic force microscopy

Design for manufacturing

Genetic algorithms


Detection and tracking algorithms

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