For data-driven intelligent systems, the data processing algorithms are very important and need to be tested adequately. Because of the high safety requirement, the cost of testing becomes very high and reducing such cost is needed. Regression test selection is an effective mean to control the scale of testing. For data-driven intelligent systems, the coincidental correctness happens frequently because of the weak dynamic information flows, and leads that the regression test sets contain a lot of redundant tests. Therefore, a regression test selection technique is proposed based on the coincidental correctness probability. This method considers the probability of coincidental correctness in addition to the code coverage. The selected tests not only cover the modified code, but have a higher probability to transfer the intermediate results produced by the modified code to the program output. Such selection can reduce the impact of coincidental correctness. The empirical results show that the proposed selection method can improve the precision of selection and reduce the size of the regression tests.