Prediction and outlier detection in classification problems.

ABSTRACT: We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction sets). BCOPS constructs a prediction set C(x) as a subset of class labels, possibly empty. It tries to optimize the out-of-sample performance, aiming to include the correct class and to detect outliers x as often as possible. BCOPS returns no prediction (corresponding to C(x) equal to the empty set) if it infers x to be an outlier. The proposed method combines supervised learning algorithms with conformal prediction to minimize a misclassification loss averaged over the out-of-sample distribution. The constructed prediction sets have

SUBMITTER: Guan L 

PROVIDER: S-EPMC9305480 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature