Active learning (machine learning): Difference between revisions

Revision as of 13:58, 14 October 2024

Revision as of 16:49, 7 December 2024

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*'''Expected error reduction''': label those points that would most reduce the model's [[generalization error]].

*'''Exponentiated Gradient Exploration for Active Learning''':<ref name="Bouneffouf(2016)" /> In this paper, the author proposes a sequential algorithm named exponentiated gradient (EG)-active that can improve any active learning algorithm by an optimal random exploration.

*'''Random Sampling:''' a sample is randomly selected.<ref name="joint_role" />

*'''Uncertainty sampling''': label those points for which the current model is least certain as to what the correct output should be.

**'''Entropy Sampling:''' The entropy formula is used on each sample, and the sample with the highest entropy is considered to be the least certain.<ref name="joint_role" />

**'''Margin Sampling:''' The sample with the smallest difference between the two highest class probabilities is considered to be the most uncertain.<ref name="joint_role" />

**'''Least Confident Sampling:''' The sample with the smallest best probability is considered to be the most uncertain.<ref name="joint_role" />

*'''Query by committee''': a variety of models are trained on the current labeled data, and vote on the output for unlabeled data; label those points for which the "committee" disagrees the most

*'''Querying from diverse subspaces or partitions''':<ref name="shubhomoydas_github"/> When the underlying model is a forest of trees, the leaf nodes might represent (overlapping) partitions of the original [[feature (machine learning)|feature space]]. This offers the possibility of selecting instances from non-overlapping or minimally overlapping partitions for labeling.

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<ref name="hybrid">{{cite journal |last1=Lughofer |first1=Edwin |title=Hybrid active learning for reducing the annotation effort of operators in classification systems |journal=Pattern Recognition |date=February 2012 |volume=45 |issue=2 |pages=884–896 |doi=10.1016/j.patcog.2011.08.009|bibcode=2012PatRe..45..884L }}</ref>

<ref name="Bouneffouf(2014)">{{cite book |first1=Djallel |last1=Bouneffouf |first2=Romain |last2=Laroche |first3=Tanguy |last3=Urvoy |first4=Raphael |last4=Féraud |first5=Robin |last5=Allesiardo |year=2014 |chapter-url=https://hal.archives-ouvertes.fr/hal-01069802 |chapter=Contextual Bandit for Active Learning: Active Thompson |doi=10.1007/978-3-319-12637-1_51 |isbn=978-3-319-12636-4 |id=HAL Id: hal-01069802 |editor=Loo, C. K. |editor2=Yap, K. S. |editor3=Wong, K. W. |editor4=Teoh, A. |editor5=Huang, K. |title=Neural Information Processing |volume=8834 |pages=405–412 |series=Lecture Notes in Computer Science |s2cid=1701357 |url=https://hal.archives-ouvertes.fr/hal-01069802/file/Contextual_Bandit_for_Active_Learning.pdf }}</ref>

<ref name="joint_role">{{cite conference <!-- Citation bot no --> |last1=Faria |first1=Bruno |last2=Perdigão |first2=Dylan |last3=Brás |first3=Joana |last4=Macedo |first4=Luis |chapter=The Joint Role of Batch Size and Query Strategy in Active Learning-Based Prediction - A Case Study in the Heart Attack Domain |title=Progress in Artificial Intelligence |series=Lecture Notes in Computer Science | conference= 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022 | date=2022 |volume=13566 |pages=464–475 |doi=10.1007/978-3-031-16474-3_38|isbn=978-3-031-16473-6 | editor1= Goreti Marreiros| editor2= Bruno Martins|editor3= Ana Paiva | editor4=Bernardete Ribeiro | editor5= Alberto Sardinha}}</ref>

<ref name="multi">{{cite conference |doi=10.1145/1557019.1557119 |isbn=978-1-60558-495-9 |chapter-url=https://www.microsoft.com/en-us/research/wp-content/uploads/2009/01/sigkdd09-yang.pdf|chapter=Effective multi-label active learning for text classification |title=Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09 |pages=917 |year=2009 |last1=Yang |first1=Bishan |last2=Sun |first2=Jian-Tao |last3=Wang |first3=Tengjiao |last4=Chen |first4=Zheng |citeseerx=10.1.1.546.9358 |s2cid=1979173 }}</ref>

<ref name="single-pass">{{Cite journal | doi=10.1007/s12530-012-9060-7 |title = Single-pass active learning with conflict and ignorance| journal=Evolving Systems| volume=3| issue=4| pages=251–271|year = 2012|last1 = Lughofer|first1 = Edwin|s2cid = 43844282}}</ref>