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In this paper, we formulate a least squares version of the one-class support vector fuzzy machine (LS one-class SVFM) which is combined with the fuzzy set theory. The parameters in the proposed algorithm, such as weight vector and bias term, are fuzzy numbers. Our model only needs to solve a system of linear equations, instead of a complex quadratic programming problem (QPP) solved in one-class SVFM. Our experiments on publicly available datasets indicate that our model has comparable classification accuracy to that of the one-class SVFM but with remarkably less computational time.

Least squares one-class support vector machine on fuzzy set