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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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Full metadata record
| DC Field | Value | Language |
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| contributor.author | Ahmad, Amir | - |
| contributor.author | Dey, Lipika | - |
| date.accessioned | 2006-03-23T06:34:14Z | - |
| date.available | 2006-03-23T06:34:14Z | - |
| date.issued | 2005 | - |
| identifier.citation | Pattern Recognition Letters, 26(1), 43-56 | en |
| identifier.uri | http://eprint.iitd.ac.in/dspace/handle/2074/1491 | - |
| description.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. | en |
| format.extent | 473578 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | Feature selection | en |
| subject | Significance of attributes | en |
| subject | Classificatory knowledge extraction | en |
| title | A feature selection technique for classificatory analysis | en |
| type | Article | en |
| Appears in Collections: | Mathematics
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Size | Format |
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| ahmadfea2005.pdf | | 462Kb | Adobe PDF | View/Open |
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