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

Title: A feature selection technique for classificatory analysis
Authors: Ahmad, Amir
Dey, Lipika
Keywords: Feature selection
Significance of attributes
Classificatory knowledge extraction
Issue Date: 2005
Citation: Pattern Recognition Letters, 26(1), 43-56
Abstract: Patterns 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.
URI: http://eprint.iitd.ac.in/dspace/handle/2074/1491
Appears in Collections:Mathematics

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