Abstract: Aiming at the shortcomings of large amount of calculation, needing prior knowledge and poor application effect of traditional feature selection criterion, and according to the trait of classification error usually occuring in intersection area between categories(Bayesian decision-making interface will pass through the intersection area), a feature selection criterion based on the overlapped probability of intersection area is put forward. The criterion selects features by calculating the probability of sample point falling into the category intersection area, and the advantages of it are calculating directly from the samples and choosing a number of features, etc. The practical application of standard machine learning data sets WINE shows that the clustering effect of feature combination selected by the criterion is better than within-category and between-category criterion. When selecting the bearing failure data, the criterion can provide several feature combination, and the selected vertical and horizontal vibration feature combinations rneet the actual needs of engineering application, which is better than the feature combination selected by within-category and between-category criterion.

Key words: Bayes error probability, Category separability, Feature selection