Pool-Based Sequential Active Learning for Regression

Active learning (AL) is a machine-learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential AL for...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 30; no. 5; pp. 1348 - 1359
Main Author: Wu, Dongrui
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Active learning
Active learning (AL)
Colleges & universities
Computational modeling
Domains
inductive learning
Labeling
Learning algorithms
Machine learning
passive sampling
Performance enhancement
Predictive models
ridge regression
Sociology
Statistics
Training
Training data
transductive learning
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Active learning (AL) is a machine-learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential AL for regression (ALR). We first propose three essential criteria that an ALR approach should consider in selecting the most useful unlabeled samples: informativeness, representativeness, and diversity, and compare four existing ALR approaches against them. We then propose a new ALR approach using passive sampling, which considers both the representativeness and the diversity in both the initialization and subsequent iterations. Remarkably, this approach can also be integrated with other existing ALR approaches in the literature to further improve the performance. Extensive experiments on 11 University of California, Irvine, Carnegie Mellon University StatLib, and University of Florida Media Core data sets from various domains verified the effectiveness of our proposed ALR approaches.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2868649