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An Active Learning Algorithm Based on the Distribution Principle of Bhattacharyya Distance

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https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F10%2F11%2F1927%2Fpdf;h=repec:gam:jmathe:v:10:y:2022:i:11:p:1927-:d:831586
Time Added
6/12/2022, 6:39:31 PM
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Machine Learning
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Authors
He Xu Chunyue Ding He Xu: School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Chunyue Ding: School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Peng Li: School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Yimu Ji: School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China
Abstract
Active learning is a method that can actively select examples with much information from a large number of unlabeled samples to query labeled by experts so as to obtain a high-precision classifier with a small number of samples. Most of the current research uses the basic principles to optimize the classifier at each iteration but the batch query with the largest amount of information in each round does not represent the overall distribution of the sample that is it may fall into partial optimization and ignore the whole which will may affect or reduce its accuracy. In order to solve this problem a special distance measurement method—Bhattacharyya Distance—is used in this paper. By using this distance and designing a new set of query decision logic we can improve the accuracy of the model. Our method embodies the query of the samples with the most representative distribution and the largest amount of information to realize the classification task based on a small number of samples. We perform theoretical proofs and experimental analysis. Finally we use different data sets and compare them with other classification algorithms to evaluate the performance and efficiency of our algorithm.
Keywords
machine learning classification ; bhattacharyya distance ; active learning (search for similar items in EconPapers)
Year Published
2022
Series
Mathematics 2022 vol. 10 issue 11 1-15
Rank
0.52
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