Improving the accuracy of the optimum-path forest supervised classifier for large datasets

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorCastelo-Fernández, César
dc.creatorDe Rezende, Pedro J.
dc.creatorFalcão, Alexandre X.
dc.creatorPapa, João Paulo
dc.date2014-05-27T11:25:25Z
dc.date2016-10-25T18:33:19Z
dc.date2014-05-27T11:25:25Z
dc.date2016-10-25T18:33:19Z
dc.date2010-12-15
dc.date.accessioned2017-04-06T01:48:46Z
dc.date.available2017-04-06T01:48:46Z
dc.identifierLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6419 LNCS, p. 467-475.
dc.identifier0302-9743
dc.identifier1611-3349
dc.identifierhttp://hdl.handle.net/11449/72224
dc.identifierhttp://acervodigital.unesp.br/handle/11449/72224
dc.identifier10.1007/978-3-642-16687-7_62
dc.identifier2-s2.0-78649978375
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-16687-7_62
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/893116
dc.descriptionIn this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.
dc.languageeng
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLearning Algorithm
dc.subjectOptimum-Path Forest Classifier
dc.subjectOutlier Detection
dc.subjectSupervised Classification
dc.subjectClassification time
dc.subjectFalse negatives
dc.subjectFalse positive
dc.subjectForest classifiers
dc.subjectLarge datasets
dc.subjectNew approaches
dc.subjectSupervised classification
dc.subjectSupervised classifiers
dc.subjectSupervised pattern recognition
dc.subjectTraining sets
dc.subjectTraining time
dc.subjectClassification (of information)
dc.subjectClassifiers
dc.subjectComputer vision
dc.subjectData mining
dc.subjectLearning algorithms
dc.titleImproving the accuracy of the optimum-path forest supervised classifier for large datasets
dc.typeOtro


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