@article{herde2021survey,
  abstract = {Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition
of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries 
annotations intelligently from annotators to train a high-performance classification model at a low 
annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, 
omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in 
real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation
mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL 
strategies have been proposed to address these issues. They differ in at least one of the following three 
central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be 
affected by various factors, such as missing expertise; 2) generalization of the interaction with human 
annotators through different query and annotation types, such as asking an annotator for feedback on an inferred 
classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. 
This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we 
introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and 
annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible 
directions for future research in the field of AL.},
  author = {Herde, Marek and Huseljic, Denis and Sick, Bernhard and Calma, Adrian},
  doi = {10.1109/ACCESS.2021.3135514},
  interhash = {67f53654f481d5ce4cb0954d975f2f3b},
  intrahash = {628e6f6c46d6a72dbd3ef5f4555d114b},
  journal = {IEEE Access},
  pages = {166970--166989},
  publisher = {IEEE},
  title = {A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification},
  url = {https://ieeexplore.ieee.org/abstract/document/9650877},
  volume = 9,
  year = 2021
}