libact: Pool-based Active Learning in Python

libact is a Python package designed to make active learning easier for general users. The package not only implements several popular active learning strategies, but also features the active-learning-by-learning meta-algorithm that assists the users to automatically select the best strategy on the fly. Furthermore, the package provides a unified interface for implementing more strategies, models and application-specific labelers. The package is open-source on Github, and can be easily installed from Python Package Index repository.

[1]  Hsuan-Tien Lin,et al.  Active Learning by Learning , 2015, AAAI.

[2]  Klaus Brinker,et al.  On Active Learning in Multi-label Classification , 2005, GfKl.

[3]  Chun-Liang Li,et al.  Active Learning Using Hint Information , 2015, Neural Computation.

[4]  Lyle H. Ungar,et al.  Machine Learning manuscript No. (will be inserted by the editor) Active Learning for Logistic Regression: , 2007 .

[5]  William A. Gale,et al.  A sequential algorithm for training text classifiers , 1994, SIGIR '94.

[6]  Rong Jin,et al.  Active Learning by Querying Informative and Representative Examples , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Arnold W. M. Smeulders,et al.  Active learning using pre-clustering , 2004, ICML.

[8]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Comparison of Active Learning Strategies and Proposal of a Multiclass Hypothesis Space Search , 2014, HAIS.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Hsuan-Tien Lin,et al.  A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[11]  Zheng Chen,et al.  Effective multi-label active learning for text classification , 2009, KDD.

[12]  Gabriel Stanovsky,et al.  Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models , 2017, EACL.

[13]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[14]  Hsuan-Tien Lin,et al.  Multi-label Active Learning with Auxiliary Learner , 2011, ACML.

[15]  Habib Fardoun,et al.  JCLAL: A Java Framework for Active Learning , 2016, J. Mach. Learn. Res..

[16]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[17]  Xin Li,et al.  Active Learning with Multi-Label SVM Classification , 2013, IJCAI.

[18]  Sanjoy Dasgupta,et al.  Hierarchical sampling for active learning , 2008, ICML '08.

[19]  H. Sebastian Seung,et al.  Query by committee , 1992, COLT '92.