Learning Hierarchical Classifiers with Class Taxonomies

dc.contributor.author Wu, Feihong
dc.contributor.author Zhang, Jun
dc.contributor.department Computer Science
dc.date 2018-02-13T23:50:30.000
dc.date.accessioned 2020-06-30T01:55:57Z
dc.date.available 2020-06-30T01:55:57Z
dc.date.issued 2005-01-01
dc.description.abstract <p>As more and more data with class taxonomies emerge in diverse fields, such as pattern recognition, text classification and gene function prediction, we need to extend traditional machine learning methods to solve classification problem in such data sets, which presents more challenges over common pattern classification problems. In this paper, we define structured label classification problem and investigate two learning approaches that can learn classifier in such data sets. We also develop distance metrics with label mapping strategy to evaluate the results. We present experimental results that demonstrate the promise of the proposed approaches.</p>
dc.identifier archive/lib.dr.iastate.edu/cs_techreports/226/
dc.identifier.articleid 1242
dc.identifier.contextkey 5463162
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath cs_techreports/226
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/20046
dc.source.bitstream archive/lib.dr.iastate.edu/cs_techreports/226/WZTech.pdf|||Fri Jan 14 22:43:51 UTC 2022
dc.subject.disciplines Artificial Intelligence and Robotics
dc.title Learning Hierarchical Classifiers with Class Taxonomies
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456
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