Abstract
A new intelligent method for disease diagnosis based on rough set theory (RST) and the relevance vector machine (RVM) for classification is presented as the rough relevance vector machine (RRVM). The RRVM mixes rough set’s strong rule extraction ability with the excellent classification ability of the relevance vector machine through preprocessing initial information, reducing data, and training the relevance vector machine. Compared with traditional intelligence methods such as neural network (NN), support vector machine (SVM), and relevance vector machine (RVM), this method manages to identify disease samples objectively and effectively with less transcendental information.
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Foundation item: Supported by the National Natural Science Foundation of China (70771708)
Biography: LI Dingfang (1965–), male, Professor, Ph. D., research direction: computational learning theory, computing in science and engineering.
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Li, D., Xiong, W. & Zhao, X. A classifier based on rough set and relevance vector machine for disease diagnosis. Wuhan Univ. J. Nat. Sci. 14, 194–200 (2009). https://doi.org/10.1007/s11859-009-0302-x
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DOI: https://doi.org/10.1007/s11859-009-0302-x
Key words
- rough set theory (RST)
- relevance vector machine (RVM)
- neural network (NN)
- support vector machine (SVM)
- disease diagnosis