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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/1491
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| 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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| ahmadfea2005.pdf | | 462Kb | Adobe PDF | View/Open |
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