Active learning for ordinal classification on incomplete data.

Existing active learning algorithms typically assume that the data provided are complete. Nonetheless, data with missing values are common in real-world applications, and active learning on incomplete data is less studied. This paper studies the problem of active learning for ordinal classification on incomplete data. Although cutting-edge imputation methods can be used to impute the missing values before commencing active learning, inaccurately imputed instances are unavoidable and may degrade the ordinal classifier's performance once labeled. Therefore, the crucial question in this work is how to reduce the negative impact of imprecisely filled instances on active learning. First, to avoid selecting filled instances with high imputation imprecision, we propose penalizing the query selection with a novel imputation uncertainty measure that combines a feature-level imputation uncertainty and a knowledge-level imputation uncertainty. Second, to mitigate the adverse influence of potentially labeled imprecisely imputed instances, we suggest using a diversity-based uncertainty sampling strategy to select query instances in specified candidate instance regions. Extensive experiments on nine public ordinal classification datasets with varying value missing rates show that the proposed approach outperforms several baseline methods. [ABSTRACT FROM AUTHOR]