Learning a credal classifier with optimized and adaptive multi-estimation for missing data imputation

Zuo-Wei Zhang (1, 2, 3) , Hong-Peng Tian (4) , Ling-Zhi Yan , Arnaud Martin (2, 3) , Kuang Zhou (1)

1 NPU - Northwestern Polytechnical University [Xi'an]
2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data
3 UR - Université de Rennes
4 Zhengzhou University

The classification analysis of missing data is still a challenging task since the training patterns may be insufficient and incomplete in many fields. To train a high-performance classifier and pursue high accuracy, we learn a credal classifier based on an optimized and adaptive multi-estimation (OAME) method for missing data imputation on training and test sets. In OAME, some incomplete training patterns are estimated as multiple versions by a global optimization method thereby expanding the training set. On the other hand, the test pattern is adaptively estimated as one or multiple versions depending on the neighbors. For the test pattern with multiple versions, the corresponding outputs with different discounting factors (weights), represented by the basic belief assignments (BBAs), are fused for final credal classification based on evidence theory. The discounting factor contains two aspects: the importance and reliability factors that are used respectively to quantify the importance of the edited version itself and to represent the reliability of the classification result of the version. The effectiveness of OAME is widely validated on several real datasets and critically compared to other related methods.

Mots clés: Missing data; credal classification; evidence theory; missing values; multiple imputation

Domaines: Informatique [cs]

Zuo-Wei Zhang, Hong-Peng Tian, Ling-Zhi Yan, Arnaud Martin, Kuang Zhou. Learning a credal classifier with optimized and adaptive multi-estimation for missing data imputation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52 (7), pp.4092-4104. ⟨hal-03271783⟩