Abstract
The performance of dimensionality reduction on multiple, relevant, and uncertain data is not satisfied by a single method. Therefore, in this paper, we proposed a two-stage reduction method based on rough set and factor analysis (RSFA). This method integrates the advantages of feature selection on treating relevant and the advantages of rough set (RS) reduction on maintaining classification power. At first, a RS reduction is used to remove superfluous and interferential attributes. Next, a factor analysis is utilized to extract common factors to replace multi-dimension attributes. Finally, the RSFA is verified by using traditional Chinese medical clinical data to predict patients’ syndrome. The result shows that less attributes and more accuracy can be expected with RSFA, which is an appropriate reduction method for such problems.
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Liu, Z., Fang, L., Yu, M., Wang, P., Yan, J. (2012). A Two-Stage Reduction Method Based on Rough Set and Factor Analysis. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_44
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DOI: https://doi.org/10.1007/978-3-642-31588-6_44
Publisher Name: Springer, Berlin, Heidelberg
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