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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1491

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dc.contributor.authorAhmad, Amir-
dc.contributor.authorDey, Lipika-
dc.identifier.citationPattern Recognition Letters, 26(1), 43-56en
dc.description.abstractPatterns summarizing mutual associations between class decisions and attribute values in a pre-classified database, provide insight into the significance of attributes and also useful classificatory knowledge. In this paper we have proposed a conditional probability based, efficient method to extract the significant attributes from a database. Reducing the feature set during pre-processing enhances the quality of knowledge extracted and also increases the speed of computation. Our method supports easy visualization of classificatory knowledge. A likelihood-based classification algorithm that uses this classificatory knowledge is also proposed. We have also shown how the classification methodology can be used for cost-sensitive learning where both accuracy and precision of prediction are important.en
dc.format.extent473578 bytes-
dc.subjectFeature selectionen
dc.subjectSignificance of attributesen
dc.subjectClassificatory knowledge extractionen
dc.titleA feature selection technique for classificatory analysisen
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