Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/340017
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dc.coverage.spatialEnhanced gene expression classification for efficient diagnosis with optimized feature selection
dc.date.accessioned2021-09-13T12:21:34Z-
dc.date.available2021-09-13T12:21:34Z-
dc.identifier.urihttp://hdl.handle.net/10603/340017-
dc.description.abstractAs the dimensionality of the data increases in machine learning, the amount of data needed to provide a reliable analysis increases exponentially. Microarray or gene expression profiling is applied to compare and determine the gene expression level and pattern for different cell types or tissue samples in a single experiment. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. The grouping of different tumors of the gene expression data is very critical in the diagnosis of cancer and the discovery of the drug and is even more possible owing to the importance in the diagnosis of cancer owing to its huge size. These DNA based micro array technologies have resulted in expressing many thousands of genes in one single experiment and for the purpose of analysing the profiles of expression. The feature selection-based methods will choose the informative genes before the classification of the data of microarray for the prediction and diagnosis of cancer. These methods remove the redundant and irrelevant features for improving the accuracy of classification. In this work, proposed the Artificial Bee Colony (ABC) based feature selection in bone marrow PC gene expression data. Swarm intelligence-based ABC algorithm has been proposed to find the best features in the gene identification. The ABC will be used for selection that generates the subset of the features and used for every feature produced by the onlookers; therefore, this proposed system will be based on the lines of wrapper-based feature selection. The main goal to this is choosing the minimum number of genes which are deemed to be very significant for that of the PCs having the improvement of the accuracy of prediction by using this proposed approach. The results have shown that this method of ABC based feature selection for that of the GDS531 has a higher accuracy of classification with an accuracy of about 2.94% compared to the GDS2643. newline
dc.format.extentxvi,125 p.
dc.languageEnglish
dc.relationp.112-124
dc.rightsuniversity
dc.titleEnhanced gene expression classification for efficient diagnosis with optimized feature selection
dc.title.alternative
dc.creator.researcherRagunthar, T
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordTelecommunications
dc.subject.keywordMachine learning
dc.subject.keywordHeuristical methods
dc.description.note
dc.contributor.guideSelvakumar, S
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
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 File128.97 kBAdobe PDFView/Open
02_certificates.pdf359.43 kBAdobe PDFView/Open
03_vivaproceedings.pdf554.81 kBAdobe PDFView/Open
04_bonafidecertificate.pdf437.86 kBAdobe PDFView/Open
05_abstracts.pdf214.47 kBAdobe PDFView/Open
06_acknowledgements.pdf214.95 kBAdobe PDFView/Open
07_contents.pdf219.85 kBAdobe PDFView/Open
08_listoftables.pdf213.81 kBAdobe PDFView/Open
09_listoffigures.pdf214.97 kBAdobe PDFView/Open
10_listofabbreviations.pdf215.06 kBAdobe PDFView/Open
11_chapter1.pdf571.28 kBAdobe PDFView/Open
12_chapter2.pdf532.54 kBAdobe PDFView/Open
13_chapter3.pdf359.27 kBAdobe PDFView/Open
14_chapter4.pdf714.55 kBAdobe PDFView/Open
15_chapter5.pdf604.68 kBAdobe PDFView/Open
16_chapter6.pdf586.66 kBAdobe PDFView/Open
17_conclusion.pdf339.19 kBAdobe PDFView/Open
18_references.pdf393.83 kBAdobe PDFView/Open
19_listofpublications.pdf416.27 kBAdobe PDFView/Open
80_recommendation.pdf91.49 kBAdobe PDFView/Open


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