Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/334222
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dc.coverage.spatialData reduction using metaheuristic algorithms
dc.date.accessioned2021-08-02T04:29:57Z-
dc.date.available2021-08-02T04:29:57Z-
dc.identifier.urihttp://hdl.handle.net/10603/334222-
dc.description.abstractOver the recent decades, the amount of data generated has been growing exponentially, the existing data mining algorithms are not feasible for processing of such huge amount of data. To solve such kind of issues, the two commonly adopted schemes are used, one is scaling up the data mining algorithms and other one is data reduction. Scaling up the data mining algorithms is not a best way, but data reduction is fairly possible. In this research work, feature selection, feature extraction and instance selection are carried out for data reduction and without downing the performance of the classification algorithms and classification accuracies. This research works conducts wide literature survey of existing instance selection, feature selection and feature extraction methods. This survey provides in depth view of existing techniques and gives a direction to rest of the study. Many approaches have been proposed for feature selection and instance selection, but most of the approaches still suffer from the problem of stagnation in local optima. Therefore, an efficient searching technique is required to address feature selection and instance selection tasks. In this research work, Cuttlefish Optimization Algorithm, Principal Component Analysis and Cuttlefish Optimization Algorithm through Tabu search are used for feature selection, feature extraction and instance selection. In the first work, instance selection is carried out by using cuttlefish optimization algorithm and the feature extraction is carried out by using principal component analysis. The obtained reduced dataset provides more or less similar detection rate, false positive rate and accuracy rate from what have obtained from using the entire dataset and it take small amount of computational time for training the classifiers newline
dc.format.extentxx,148p.
dc.languageEnglish
dc.relationp.139-147
dc.rightsuniversity
dc.titleData reduction using metaheuristic algorithms
dc.title.alternative
dc.creator.researcherKarunakaran, V
dc.subject.keywordData reduction
dc.subject.keywordData mining algorithms
dc.subject.keywordClassification algorithms
dc.description.note
dc.contributor.guideSuganthi, M
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2019
dc.date.awarded2019
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File142.18 kBAdobe PDFView/Open
02_certificates.pdf196.57 kBAdobe PDFView/Open
03_vivaproceedings.pdf299.08 kBAdobe PDFView/Open
04_bonafidecertificate.pdf237.1 kBAdobe PDFView/Open
05_abstracts.pdf717.31 kBAdobe PDFView/Open
06_acknowledgements.pdf236.46 kBAdobe PDFView/Open
07_contents.pdf765.77 kBAdobe PDFView/Open
08_listoftables.pdf761.28 kBAdobe PDFView/Open
09_listoffigures.pdf800.19 kBAdobe PDFView/Open
10_listofabbreviations.pdf762.19 kBAdobe PDFView/Open
11_chapter1.pdf878.64 kBAdobe PDFView/Open
12_chapter2.pdf908.51 kBAdobe PDFView/Open
13_chapter3.pdf1.32 MBAdobe PDFView/Open
14_chapter4.pdf1.14 MBAdobe PDFView/Open
15_chapter5.pdf991.43 kBAdobe PDFView/Open
16_chapter6.pdf943.92 kBAdobe PDFView/Open
17_conclusion.pdf721.88 kBAdobe PDFView/Open
18_references.pdf941.43 kBAdobe PDFView/Open
19_listofpublications.pdf796.4 kBAdobe PDFView/Open
80_recommendation.pdf148.56 kBAdobe PDFView/Open


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