Fuzzy-belief k-nearest neighbor classifier for uncertain data

Abstract : Information fusion technique like evidence theory has been widely applied in the data classification to improve the performance of classifier. A new fuzzy-belief K-nearest neighbor (FBK-NN) classifier is proposed based on evidential reasoning for dealing with uncertain data. In FBK-NN, each labeled sample is assigned with a fuzzy membership to each class according to its neighborhood. For each input object to classify, K basic belief assignments (BBA's) are determined from the distances between the object and its K nearest neighbors taking into account the neighbors' memberships. The K BBA's are fused by a new method and the fusion results are used to finally decide the class of the query object. FBK-NN method works with credal classification and discriminate specific classes, metaclasses and ignorant class. Meta-classes are defined by disjunction of several specific classes and they allow to well model the partial imprecision of classification of the objects. The introduction of meta-classes in the classification procedure reduces the misclassification errors. The ignorant class is employed for outliers detections. The effectiveness of FBK-NN is illustrated through several experiments with a comparative analysis with respect to other classical methods.
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Communication dans un congrès
Fusion 2014 : 17th International Conference on Information Fusion, Jul 2014, Salamanca, Spain. pp.1-8
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https://hal-onera.archives-ouvertes.fr/hal-01070480
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Soumis le : mercredi 1 octobre 2014 - 14:43:46
Dernière modification le : vendredi 22 juin 2018 - 01:18:55
Document(s) archivé(s) le : vendredi 2 janvier 2015 - 11:06:08

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DTIM14032.1404207568.pdf
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  • HAL Id : hal-01070480, version 1

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Zhun-Ga Liu, Quam Pan, Jean Dezert, Grégoire Mercier, Yong Liu. Fuzzy-belief k-nearest neighbor classifier for uncertain data. Fusion 2014 : 17th International Conference on Information Fusion, Jul 2014, Salamanca, Spain. pp.1-8. 〈hal-01070480〉

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