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dc.contributor.authorPradhananga, Nripendraen_NZ
dc.date.accessioned2007-07-30T15:20:19Z
dc.date.available2008-01-10T16:21:52Z
dc.date.issued2007en_NZ
dc.identifier.citationPradhananga, N. (2007). Effective Linear-Time Feature Selection (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2315en
dc.identifier.urihttps://hdl.handle.net/10289/2315
dc.description.abstractThe classification learning task requires selection of a subset of features to represent patterns to be classified. This is because the performance of the classifier and the cost of classification are sensitive to the choice of the features used to construct the classifier. Exhaustive search is impractical since it searches every possible combination of features. The runtime of heuristic and random searches are better but the problem still persists when dealing with high-dimensional datasets. We investigate a heuristic, forward, wrapper-based approach, called Linear Sequential Selection, which limits the search space at each iteration of the feature selection process. We introduce randomization in the search space. The algorithm is called Randomized Linear Sequential Selection. Our experiments demonstrate that both methods are faster, find smaller subsets and can even increase the classification accuracy. We also explore the idea of ensemble learning. We have proposed two ensemble creation methods, Feature Selection Ensemble and Random Feature Ensemble. Both methods apply a feature selection algorithm to create individual classifiers of the ensemble. Our experiments have shown that both methods work well with high-dimensional data.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectfilteren_NZ
dc.subjectwrapperen_NZ
dc.subjectfeature selectionen_NZ
dc.subjectattribute selectionen_NZ
dc.subjectensemble learningen_NZ
dc.subjectmachine learningen_NZ
dc.subjectLinear Feature Selectionen_NZ
dc.titleEffective Linear-Time Feature Selectionen_NZ
dc.typeThesisen_NZ
thesis.degree.disciplineComputer Scienceen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2007-07-30T15:20:19Zen_NZ
uow.date.available2008-01-10T16:21:52Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20070730.152019en_NZ
uow.date.migrated2009-06-09T23:31:25Zen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ


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