SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers
作者：
Highlights：
• A new adaptative classifier aggregation method in the possibility theory framework.
• Scalability with a large number of classifiers.
• Robustness statistical property with respect to noisy classifier predictions.
• Robustness statistical property with respect to adversarial classifiers.
• Robustness statistical property with respect to redundant classifier predictions.
摘要
•A new adaptative classifier aggregation method in the possibility theory framework.•Scalability with a large number of classifiers.•Robustness statistical property with respect to noisy classifier predictions.•Robustness statistical property with respect to adversarial classifiers.•Robustness statistical property with respect to redundant classifier predictions.
论文关键词：Robust classifier combination,Agnostic aggregation,Information fusion,Classification,Possibility theory
论文评审过程：Received 13 September 2019, Revised 30 December 2019, Accepted 19 February 2020, Available online 21 February 2020, Version of Record 28 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113332
