In this paper, using chance constrained programming formulation, a new chance constrained twin support vector machine (CC-TWSVM) is proposed. This paper studies twin support vector machine classification when data points are uncertain with measurement statistically noise. With some properties known for the distribution, the CC-TWSVM model aims to ensure the small probability of error classification for the uncertain data. We also provide equivalent second-order cone programming (SOCP) model of the CC-TWSVM model by the properties of moment information of uncertain data. The dual problem of SOCP model is introduced and the optimal value of the CC-TWSVM model can be solved directly. In addition, we also show the performance of CC-TWSVM model in artificial data and real data by numerical experiments.
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