The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Pairwise classification and support vector machines
Advances in kernel methods
Fault diagnosis using Rough Sets Theory
Computers in Industry
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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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.