Heterogeneous Uncertainty Sampling for Supervised Learning

David D. Lewis, Jason Catlett

Abstract: Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Previous chapter in book Next chapter in book Recommended articles Citing articles (0) Copyright © 1994 Morgan Kaufmann Publishers, Inc. Published by Elsevier Inc. All rights reserved. Recommended articles Automatic image annotation via compact graph based semi-supervised learning Knowledge-Based Systems, Volume 76, 2015, pp. 148-165 Download PDF View details Learning from class-imbalanced data: Review of methods and applications Expert Systems with Applications, Volume 73, 2017, pp. 220-239 Download PDF View details An efficient active learning method for multi-task learning Knowledge-Based Systems, 2019, Article 105137 Download PDF View details 1 2 Next Citing articles (0) Article Metrics Citations Citation Indexes: 259 Captures Readers: 260 View details Elsevier About ScienceDirect Remote access Shopping cart Advertise Contact and support Terms and conditions Privacy policy We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies . Copyright © 2020 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. {"abstracts":{"content":[{"$$":[{"$":{"id":"cesectitle1"},"#name":"section-title","_":"Abstract"},{"$$":[{"$$":[{"#name":"italic","_":"Uncertainty sampling"},{"#name":"__text__","_":" methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this "},{"#name":"italic","_":"heterogeneous"},{"#name":"__text__","_":" approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger."}],"$":{"view":"all","id":"spara1"},"#name":"simple-para"}],"$":{"view":"all","id":"abssec1"},"#name":"abstract-sec"}],"$":{"xmlns:ce":true,"view":"all","id":"abs1","class":"author"},"#name":"abstract"}],"floats":[],"footnotes":[],"attachments":[]},"accessOptions":{},"adobeTarget":{"variation":"control"},"article":{"publication-content":{"publicationCity":"San Francisco (CA)","publisherName":"Morgan Kaufmann","coverImageUrl":"https://ars.els-cdn.com/content/image/Dmorgank.gif","transactionsBlocked":"false","coverThumbnailUrl":"3-s2.0-B9781558603356-cov150h.gif","specialCoverImage":"3-s2.0-C20090275428-cov150h.gif","sourceOpenAccess":false,"publicationCoverImageUrl":"https://ars.els-cdn.com/content/image/3-s2.0-C20090275428-cov150h.gif"},"pii":"B978155860335650026X","dates":{"Available online":"27 June 2014","Revised":[],"Publication date":"1 January 1994"},"access":{"openArchive":false,"openAccess":false},"crawlerInformation":{"canCrawlPDFContent":false,"isCrawler":false},"document-references":36,"accessOptions":{"accessHeader":{"parameters":[],"key":"access_header_no_remote_access"},"outwardLinksSection":{"linkingHubUrl":"https://linkinghub.elsevier.com/retrieve/pii/B978155860335650026X?showall