Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey

Generally, data is available abundantly in unlabeled form, and its annotation requires some cost. The labeling, as well as learning cost, can be minimized by learning with the minimum labeled data instances. Active learning (AL), learns from a few labeled data instances with the additional facility...

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Vydané v:Journal of computer science and technology Ročník 35; číslo 4; s. 913 - 945
Hlavní autori: Kumar, Punit, Gupta, Atul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Singapore 01.07.2020
Springer
Springer Nature B.V
Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh 482005, India
Predmet:
Active learning
Annotations
Artificial Intelligence
Classification
Clustering
Computational linguistics
Computer Science
Data Structures and Information Theory
Deep learning
Information Systems Applications (incl.Internet)
Labels
Language processing
Mathematical analysis
Natural language interfaces
Numerical analysis
Queries
Software Engineering
Survey
Surveys
Theory of Computation
deep active learning
active regression
active learning
active classification
active learning query strategy
active clustering
ISSN:1000-9000, 1860-4749
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Shrnutí:Generally, data is available abundantly in unlabeled form, and its annotation requires some cost. The labeling, as well as learning cost, can be minimized by learning with the minimum labeled data instances. Active learning (AL), learns from a few labeled data instances with the additional facility of querying the labels of instances from an expert annotator or oracle. The active learner uses an instance selection strategy for selecting those critical query instances, which reduce the generalization error as fast as possible. This process results in a refined training dataset, which helps in minimizing the overall cost. The key to the success of AL is query strategies that select the candidate query instances and help the learner in learning a valid hypothesis. This survey reviews AL query strategies for classification, regression, and clustering under the pool-based AL scenario. The query strategies under classification are further divided into: informative-based, representative-based, informative- and representative-based, and others. Also, more advanced query strategies based on reinforcement learning and deep learning, along with query strategies under the realistic environment setting, are presented. After a rigorous mathematical analysis of AL strategies, this work presents a comparative analysis of these strategies. Finally, implementation guide, applications, and challenges of AL are discussed.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-020-9487-4