Learning a credal classifier with optimized and adaptive multi-estimation for missing data imputation
| Autor: | Hong-Peng Tian, Arnaud Martin, Ling-Zhi Yan, Zuowei Zhang, Kuang Zhou |
|---|---|
| Přispěvatelé: | Northwestern Polytechnical University [Xi'an] (NPU), Declarative & Reliable management of Uncertain, user-generated Interlinked Data (DRUID), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Zhengzhou University, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UR) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
multiple imputation
Computer science Missing data Machine learning computer.software_genre Task (project management) Classifier (linguistics) [INFO]Computer Science [cs] Electrical and Electronic Engineering Global optimization Reliability (statistics) Estimation Discounting business.industry Computer Science Applications Test (assessment) Human-Computer Interaction missing values evidence theory Control and Systems Engineering credal classification Artificial intelligence business computer Software |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE, In press IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52 (7), pp.4092-4104 |
| ISSN: | 2168-2216 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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