A classifier based on rough set and relevance vector machine for disease diagnosis |
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Authors: | Dingfang Li Wei Xiong Xiang Zhao |
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Institution: | (1) School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, Hubei, China;(2) Radar and Avionics Institute of Aviation Industry Corporation of China, Wuxi, 214063, Jiangsu, China;(3) College of Mathematics and Information Science, Xinyang Normal University, Xinyang, 464000, Henan, China |
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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.
Biography: LI Dingfang (1965–), male, Professor, Ph. D., research direction: computational learning theory, computing in science and
engineering. |
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Keywords: | rough set theory (RST) relevance vector machine (RVM) neural network (NN) support vector machine (SVM) disease diagnosis |
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