Une méthode possibiliste de discrimination adaptée aux classes de forme complexe
Our team works on the classification of data coming from industrial and medical sectors, in order to develop decision making and diagnosis systems. In this paper we propose to modify the fuzzy method of pattern matching, in order to classify data including classes of complex shape. We describe the b...
Gorde:
| Argitaratua izan da: | URI:https://journals.openedition.org/msh, |
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| Egile Nagusiak: | , , |
| Formatua: | Article ou chapitre numérique |
| Hizkuntza: | Français |
| Argitaratua: |
Mathématiques et sciences humaines
2006
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| Gaiak: | |
| Sarrera elektronikoa: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Gaia: | Our team works on the classification of data coming from industrial and medical sectors, in order to develop decision making and diagnosis systems. In this paper we propose to modify the fuzzy method of pattern matching, in order to classify data including classes of complex shape. We describe the basic method before showing its limits when classes are not convex. Then, we propose to improve the method by introducing a multiprototype approach. We present an industrial example, which consists in sorting automatically plastic bottles in order to recycle them. Finally, we compare the results obtained by this method with those given by the fuzzy k-nearest neighbours method, using three types of data : plastic bottles, iris and waveform data. |
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