Learning feature-projection based classifiers

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dc.citation.epage4544en_US
dc.citation.issueNumber4en_US
dc.citation.spage4532en_US
dc.citation.volumeNumber39en_US
dc.contributor.authorDayanik, A.en_US
dc.date.accessioned2015-07-28T12:00:13Z
dc.date.available2015-07-28T12:00:13Z
dc.date.issued2012-03en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThis paper aims at designing better performing feature-projection based classification algorithms and presents two new such algorithms. These algorithms are batch supervised learning algorithms and represent induced classification knowledge as feature intervals. In both algorithms, each feature participates in the classification by giving real-valued votes to classes. The prediction for an unseen example is the class receiving the highest vote. The first algorithm, OFP.MC, learns on each feature pairwise disjoint intervals which minimize feature classification error. The second algorithm. GFP.MC, constructs feature intervals by greedily improving the feature classification error. The new algorithms are empirically evaluated on twenty datasets from the UCI repository and are compared with the existing feature-projection based classification algorithms (FILIF, VFI5, CFP, k-NNFP, and NBC). The experiments demonstrate that the OFP.MC algorithm outperforms other feature-projection based classification algorithms. The GFP.MC algorithm is slightly inferior to the OFP.MC algorithm, but, if it is used for datasets with large number of instances, then it reduces the space requirement of the OFP.MC algorithm. The new algorithms are insensitive to boundary noise unlike the other feature-projection based classification algorithms considered here. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2011.09.133en_US
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/11693/12133
dc.language.isoEnglishen_US
dc.publisherPergamon Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.eswa.2011.09.133en_US
dc.source.titleExpert Systems with Applications: an international journalen_US
dc.subjectClassification learningen_US
dc.subjectInductive learningen_US
dc.subjectFeature projectionsen_US
dc.titleLearning feature-projection based classifiersen_US
dc.typeArticleen_US
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