Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data

Active learning is an important learning paradigm in machine learning and data mining, which aims to train effective classifiers with as few labeled samples as possible. Querying discriminative (informative) and representative samples are the state-of-the-art approach for active learning. Fully util...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 32; no. 9; pp. 4111 - 4122
Main Authors: Gu, Bin, Zhai, Zhou, Deng, Cheng, Huang, Heng
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Active learning
Algorithms
Benchmark testing
Classifiers
Data mining
Datasets
Information science
Kernel
Learning algorithms
Linear programming
Machine learning
maximum mean discrepancy (MMD)
Objective function
path-following algorithm
Performance enhancement
Semisupervised learning
semisupervised support vector machine
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Active learning is an important learning paradigm in machine learning and data mining, which aims to train effective classifiers with as few labeled samples as possible. Querying discriminative (informative) and representative samples are the state-of-the-art approach for active learning. Fully utilizing a large amount of unlabeled data provides a second chance to improve the performance of active learning. Although there have been several active learning methods proposed by combining with semisupervised learning, fast active learning with fully exploiting unlabeled data and querying discriminative and representative samples is still an open question. To overcome this challenging issue, in this article, we propose a new efficient batch mode active learning algorithm. Specifically, we first provide an active learning risk bound by fully considering the unlabeled samples in characterizing the informativeness and representativeness. Based on the risk bound, we derive a new objective function for batch mode active learning. After that, we propose a wrapper algorithm to solve the objective function, which essentially trains a semisupervised classifier and selects discriminative and representative samples alternately. Especially, to avoid retraining the semisupervised classifier from scratch after each query, we design two unique procedures based on the path-following technique, which can remove multiple queried samples from the unlabeled data set and add the queried samples into the labeled data set efficiently. Extensive experimental results on a variety of benchmark data sets not only show that our algorithm has a better generalization performance than the state-of-the-art active learning approaches but also show its significant efficiency.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3016928