Instance-based regression by partitioning feature projections

dc.citation.epage79en_US
dc.citation.issueNumber1en_US
dc.citation.spage57en_US
dc.citation.volumeNumber21en_US
dc.contributor.authorUysal, İ.en_US
dc.contributor.authorGüvenir, H. A.en_US
dc.date.accessioned2016-02-08T10:26:42Z
dc.date.available2016-02-08T10:26:42Zen_US
dc.date.issued2004en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractA new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs well on classification tasks, it does not perform as well on regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP) which is designed to meet the requirement for a lazy method that achieves high levels of accuracy on regression problems. RPFP gives better performance than well-known eager approaches found in machine learning and statistics such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it is a projection-based approach that can handle interactions. We show that it outperforms existing eager or lazy approaches on many domains when there are many missing values in the training data.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:26:42Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2004en_US
dc.identifier.doi10.1023/B:APIN.0000027767.87895.b2en_US
dc.identifier.issn0924-669Xen_US
dc.identifier.issn1573-7497en_US
dc.identifier.urihttp://hdl.handle.net/11693/24272en_US
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttp://dx.doi.org/10.1023/B:APIN.0000027767.87895.b2en_US
dc.source.titleApplied Intelligenceen_US
dc.subjectFeature Projectionsen_US
dc.subjectMachine Learningen_US
dc.subjectRegressionen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer Operating Systemsen_US
dc.subjectData Reductionen_US
dc.subjectDecision Theoryen_US
dc.subjectLeast Squares Approximationsen_US
dc.subjectRegression Analysisen_US
dc.subjectFeature Projectionsen_US
dc.subjectRegression Tree Induction Systemsen_US
dc.subjectLearning Systemsen_US
dc.titleInstance-based regression by partitioning feature projectionsen_US
dc.typeArticleen_US

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