MELIDIS: Pattern recognition by intrinsic/discriminant dual modeling based on a hierarchical organization of fuzzy inference systems

Abstract : In this article, we present a new recognition approach which aim is to combine properties that are rarely fully satisfied in the same classifier: performances, robustness, genericity, compactness and transparency for the designer. This last point makes the system easier to maintain and optimize for a given application. The classifier is totally data driven and its architecture is based on the properties of the knowledge used. The main originality comes from a specific cooperation of intrinsic and discriminant knowledge. The system is organized in two levels: the first one models classes with intrinsic fuzzy prototypes and the second one operates a discrimination by fuzzy decision trees. To improve the discrimination process, this one focuses on contexts induced by a focus mechanism based on intrinsic knowledge. For transparency, the system is formalized by fuzzy inference systems combined for decision. Experiments on severals problems have shown that the system's performances are close to SVM ones, with 10 to 30 times less parameters. They have also demonstrated the interest of the collaboration between intrinsic and discriminant knowledge.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01191724
Contributor : Nicolas Ragot <>
Submitted on : Wednesday, September 2, 2015 - 2:03:52 PM
Last modification on : Tuesday, July 2, 2019 - 4:02:03 PM

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  • HAL Id : hal-01191724, version 1

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Nicolas Ragot, Eric Anquetil. MELIDIS: Pattern recognition by intrinsic/discriminant dual modeling based on a hierarchical organization of fuzzy inference systems. 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, (IPMU'04), Jul 2004, Pérouse, Italy. pp.2069-2076. ⟨hal-01191724⟩

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