Abstract
Support vector machines (SVM) and rough sets theory (RST) are two emerging techniques in data analysis. The RST can deal with vague data and remove redundant attributes without losing any information of the data; and SVM has powerful classification ability. In this study, the RST is employed to reduce data attributes. Then, the reduced attributes are used by the SVM model for classification. An example of diesel engine diagnosis in the literature is used to demonstrate the diagnosis ability of the proposed RSSVM (rough set theory with support vector machines) model. In terms of classification accuracy and efficiency, experimental outcomes show that the RSSVM model can provide better diagnosis results than those obtained by the directed acyclic graph support vector machine (DAGSVM) model.
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Pai, PF., Huang, YY. (2007). Using Support Vector Machines and Rough Sets Theory for Classifying Faulty Types of Diesel Engine. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_45
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DOI: https://doi.org/10.1007/978-3-540-74472-6_45
Publisher Name: Springer, Berlin, Heidelberg
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