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Online feature selection and classification with incomplete data

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dc.creator Kalkan, Habil
dc.date 2014-11-01T01:00:00Z
dc.date.accessioned 2021-12-03T11:30:12Z
dc.date.available 2021-12-03T11:30:12Z
dc.identifier 6d929e48-8c34-43f5-bcf7-04573a2e665b
dc.identifier 10.3906/elk-1301-181
dc.identifier https://avesis.sdu.edu.tr/publication/details/6d929e48-8c34-43f5-bcf7-04573a2e665b/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/92435
dc.description This paper presents a classification system in which learning, feature selection, and classification for incomplete data are simultaneously carried out in an online manner. Learning is conducted on a predefined model including the class-dependent mean vectors and correlation coefficients, which are obtained by incrementally processing the incoming observations with missing features. A nearest neighbor with a Gaussian mixture model, whose parameters are also estimated from the trained model, is used for classification. When a testing observation is received, the algorithm discards the missing attributes on the observation and ranks the available features by performing feature selection on the model that has been trained so far. The developed algorithm is tested on a benchmark dataset. The effect of missing features for online feature selection and classification are discussed and presented. The algorithm easily converges to the stable state of feature selection with similar accuracy results as those when using the complete and incomplete feature set with up to 50% missing data.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title Online feature selection and classification with incomplete data
dc.type info:eu-repo/semantics/article


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