The nature of statistical learning theory
The nature of statistical learning theory
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Information Sciences—Informatics and Computer Science: An International Journal
An axiomatic characterization of a fuzzy generalization of rough sets
Information Sciences—Informatics and Computer Science: An International Journal
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Ruggedness measures of medical time series using fuzzy-rough sets and fractals
Pattern Recognition Letters
Fuzzy rough sets hybrid scheme for breast cancer detection
Image and Vision Computing
On Representing and Generating Kernels by Fuzzy Equivalence Relations
The Journal of Machine Learning Research
On linear separability of data sets in feature space
Neurocomputing
A rough margin based support vector machine
Information Sciences: an International Journal
Fuzzy reasoning based on a new fuzzy rough set and its application to scheduling problems
Computers & Mathematics with Applications
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Geometrical interpretation and applications of membership functions with fuzzy rough sets
Fuzzy Sets and Systems
Fuzzy rough based regularization in Generalized Multiple Kernel Learning
Computers & Mathematics with Applications
Structure of feature spaces related to fuzzy similarity relations as kernels
Fuzzy Sets and Systems
An improved algorithm for calculating fuzzy attribute reducts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper aims to improve hard margin support vector machines (SVMs) by considering the membership of every training sample in constraints. The membership is computed by employing the technique of fuzzy rough sets so that hard margin SVMs can be combined with fuzzy rough sets and the inconsistence between conditional features and decision labels can be taken into account at the same time. In this paper, we first propose fuzzy transitive kernel based fuzzy rough sets. For binary classification, we use a lower approximation operator in fuzzy transitive kernel based fuzzy rough sets to compute the membership for every training input. And then we reformulate hard margin support vector machines into fuzzy rough set based SVMs (FRSVMs) with new constraints in which the membership is taken into account. Finally, comparisons with soft margin SVMs and fuzzy SVMs are made. The experimental results show that the proposed approach is feasible and valid. It significantly improved the performance of the hard margin SVMs.