arXiv:2409.11033
Expert Classification Aggregation
원문 초록
We consider the problem where a set of individuals has to classify $m$ objects into $p$ categories by aggregating the individual classifications, and no category can be left empty. An aggregator satisfies \emph{Expertise} if individuals are decisive either over the classification of a given object, or the classification into a given category. We show that requiring an aggregator to satisfy \emph{Expertise} (or variants of it) and be either unanimous or independent leads to numerous impossibility results.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
Hybrid-View Attention for csPCa Classification in TRUS
One Prototype Is Enough: Single-Prototype Activation for Interpretable Image Classification
Brain-inspired interpretable reservoir computing with resonant recurrent neural networks
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
토론
이 논문과 연결된 포럼 글이 아직 없습니다.