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A feature selection technique for classificatory analysis
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DOI：10.1016/j.patrec.2004.08.015.png)
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摘要
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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. (C) 2004 Elsevier B.V. All rights reserved.
Keyword:
feature selection
significance of attributes
classificatory knowledge extraction
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3.3
论文数:
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被引数:
1.6W
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