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Abstract

In this paper, via chance constrained programming formulation and fuzzy membership, we give suggestions on a new fuzzy chance constrained least squares twin support vector machine, which can make data measurement noise efficiently. In this paper, we concentrate on least squares twin support vector machine classification when data distributions are uncertain statistically. The model’s function is used to guarantee the small probability of misclassification for the uncertain data, with some known characters of the distribution. The fuzzy chance constrained least squares twin support vector machine model can be transformed into second-order cone programming (SOCP) through the properties of moment information of uncertain data and thus the dual problem of SOCP model is introduced. Besides, through the numerical experiments we also demonstrate the model’s performance in real data and artificial data.

Keywords

Support vector machine robust optimization chance constraints uncertain classification

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Fuzzy chance constrained least squares twin support vector machine for uncertain classification 1

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