*'''User Centered Labeling Strategies:''' Learning is accomplished by applying dimensionality reduction to graphs and figures like scatter plots. Then the user is asked to label the compiled data (categorical, numerical, relevance scores, relation between two instances.<ref name=":3">{{Cite journal |last1=Bernard |first1=Jürgen |last2=Zeppelzauer |first2=Matthias |last3=Lehmann |first3=Markus |last4=Müller |first4=Martin |last5=Sedlmair |first5=Michael |date=June 2018 |title=Towards User-Centered Active Learning Algorithms |url= |journal=Computer Graphics Forum |volume=37 |issue=3 |pages=121–132 |doi=10.1111/cgf.13406 |s2cid=51875861 |issn=0167-7055}}</ref>
A wide variety of algorithms have been studied that fall into these categories.<ref name="settles" /><ref name="olsson" />. While the traditional AL strategies can achieve remarkable performance, it is often challenging to predict in advance which strategy is the most suitable in aparticular situation. In recent years, meta-learning algorithms have been gaining in popularity. Some of them have been proposed to tackle the problem of learning AL strategies instead of relying on manually designed strategies. A benchmark which compares 'meta-learning approaches to active learning' to 'traditional heuristic-based Active Learning' may give intuitions if 'Learning active learning' is at the crossroads <ref>{{cite journal |last1=Desreumaux |first1=Louis |last2=Lemaire|first2=Vincent|title=Learning Active Learning at the Crossroads? Evaluation and Discussion |date=2020 |issue=Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases {(ECML} {PKDD} 2020), Ghent, Belgium,2020 |url=https://www.semanticscholar.org/paper/Learning-Active-Learning-at-the-Crossroads-and-Desreumaux-Lemaire/0814f297864b01ba85f141c4264c8c2cb5b6ad2c}}</ref>
