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dc.contributor.author Shang, Changjing
dc.contributor.author Shen, Qiang
dc.date.accessioned 2008-07-03T10:39:49Z
dc.date.available 2008-07-03T10:39:49Z
dc.date.issued 2008-07-03
dc.identifier.citation Shang , C & Shen , Q 2008 , ' Aiding neural network based image classification with fuzzy-rough feature selection ' pp. 976-982 . en
dc.identifier.other PURE: 85805
dc.identifier.other dspace: 2160/597
dc.identifier.uri http://hdl.handle.net/2160/597
dc.description C. Shang and Q. Shen. Aiding neural network based image classification with fuzzy-rough feature selection. Proceedings of the 17th International Conference on Fuzzy Systems, pp. 976-982. en
dc.description.abstract This paper presents a methodological approach for developing image classifiers that work by exploiting the technical potential of both fuzzy-rough feature selection and neural network-based classification. The use of fuzzy-rough feature selection allows the induction of low-dimensionality feature sets from sample descriptions of real-valued feature patterns of a (typically much) higher dimensionality. The employment of a neural network trained using the induced subset of features ensures the runtime classification performance. The reduction of feature sets reduces the sensitivity of such a neural network-based classifier to its structural complexity. It also minimises the impact of feature measurement noise to the classification accuracy. This work is evaluated by applying the approach to classifying real medical cell images, supported with comparative studies. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Aiding neural network based image classification with fuzzy-rough feature selection en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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