Abstract
The classical attribute reduction algorithm and its extended algorithms base on information systems with decision attributes and can not be applied to attribute reduction of no decision attributes information systems. So, based on rough set theory, the results of two condition attribute sets are compared with the method of set pair analysis in this paper, and then classification contribution degree from one to the other and classification closeness function between the two sets are constructed. A new method of attribute reduction based on classification closeness degree is given. Finally, a case study proved that this method is feasible.
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Mi, C., Yang, Y., Xu, J. (2010). A New Attribute Reduction Algorithm Based on Classification Closeness Function. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Communications in Computer and Information Science, vol 106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16339-5_69
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DOI: https://doi.org/10.1007/978-3-642-16339-5_69
Publisher Name: Springer, Berlin, Heidelberg
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