ALiPy provides a module based implementation of active learning framework, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods. It implementations more than 20 algorithms and also supports users to easily implement their own approaches under different settings.Get started
Tang, Y.-P.; Li, G.-X.; and Huang, S.-J. ALiPy: Active learning in python. https://arxiv.org/abs/1901.03802. 2019.
ALiPy provide several commonly used strategies in different active learning settings for now, and new algorithms will be added in subsequent updates.
|AL with Instance Selection|
|Uncertainty (SIGIR 1994)||Graph Density (CVPR 2012)||QUIRE (TPAMI 2014)||SPAL (AAAI 2019)|
|Query By Committee (ICML 1998)||Random||BMDR (KDD 2013)||LAL (NIPS 2017)|
|Expected Error Reduction (ICML 2001)|
|AL for Multi-Label Data|
|AUDI (ICDM 2013)||QUIRE (TPAMI 2014)||Random||MMC (KDD 2009)|
|Adaptive (IJCAI 2013)|
|AL by Querying Features|
|AFASMC (KDD 2018)||Stability (ICDM 2013)||Random|
|AL with Different Costs|
|HALC (IJCAI 2018)||Random||Cost performance|
|AL with Noisy Oracles|
|CEAL (IJCAI 2017)||IEthresh (KDD 2009)||All||Random|
|AL with Novel Query Types|
|AURO (IJCAI 2015)|
|AL for Large Scale Tasks|
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