An eager regression method based on best feature projections

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dc.citation.epage226en_US
dc.citation.spage217en_US
dc.citation.volumeNumber2070en_US
dc.contributor.authorAydın, Tolgaen_US
dc.contributor.authorGüvenir, H. Altayen_US
dc.coverage.spatialBudapest, Hungaryen_US
dc.date.accessioned2016-02-08T11:58:14Z
dc.date.available2016-02-08T11:58:14Zen_US
dc.date.issued2001en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: IEA/AIE: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systemsen_US
dc.descriptionDate of Conference: 4–7 June 2001en_US
dc.description.abstractThis paper describes a machine learning method, called Regression by Selecting Best Feature Projections (RSBFP). In the training phase, RSBFP projects the training data on each feature dimension and aims to find the predictive power of each feature attribute by constructing simple linear regression lines, one per each continuous feature and number of categories per each categorical feature. Because, although the predictive power of a continuous feature is constant, it varies for each distinct value of categorical features. Then the simple linear regression lines are sorted according to their predictive power. In the querying phase of learning, the best linear regression line and thus the best feature projection are selected to make predictions. © Springer-Verlag Berlin Heidelberg 2001.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T11:58:14Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2001en
dc.identifier.doi10.1007/3-540-45517-5_25en_US
dc.identifier.doi10.1007/3-540-45517-5en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27626en_US
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttps://doi.org/10.1007/3-540-45517-5_25en_US
dc.relation.isversionofhttps://doi.org/10.1007/3-540-45517-5en_US
dc.source.titleEngineering of Intelligent Systemsen_US
dc.subjectFeature Projectionen_US
dc.subjectPredictionen_US
dc.subjectRegressionen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectExpert Systemsen_US
dc.subjectForecastingen_US
dc.subjectIntelligent Systemsen_US
dc.subjectLearning Systemsen_US
dc.subjectLinear Regressionen_US
dc.subjectCategorical Featuresen_US
dc.subjectContinuous Featuresen_US
dc.subjectFeature Attributesen_US
dc.subjectFeature Dimensionsen_US
dc.subjectFeature Projectionen_US
dc.subjectMachine Learning Methodsen_US
dc.subjectRegressionen_US
dc.subjectSimple Linear Regressionen_US
dc.subjectRegression Analysisen_US
dc.titleAn eager regression method based on best feature projectionsen_US
dc.typeConference Paperen_US

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