Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/305676
Title: Enhanced Feature Selection Algorithm for Effective Supervised and Unsupervised Learning
Researcher: Hemavathi.D
Guide(s): Srimathi.H
Keywords: Computer Science
Computer Science Hardware and Architecture
Engineering and Technology
University: SRM University
Completed Date: 2020
Abstract: In machine learning applications, the dimension of input data is high with newlineredundancy and noisiness due to which model accuracy is highly affected in large data newlinesets. The model accuracy can be improved through dimensionality reduction by newlineminimizing the dimensions of original dataset and by eliminating redundant or newlineirrelevant information. Dimensionality reduction is realized by feature selection or newlinefeature extraction. newlineIn both supervised and unsupervised learning, many optimal feature newlineselection methods are used to find the most important variables or parameters which newlineare necessary in predicting the outcomes. Also feature selection helps to train the newlinealgorithm faster as well as reduces the model complexity. Although the number of newlineattributes available is more, only limited features will helps to enhance the accuracy of newlinethe model in both the types of learning newline
Pagination: 
URI: http://hdl.handle.net/10603/305676
Appears in Departments:Department of Computer Science Engineering

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80_recommendation.pdfAttached File517.46 kBAdobe PDFView/Open
abstract.pdf260.9 kBAdobe PDFView/Open
acknowledgement.pdf264.87 kBAdobe PDFView/Open
chapter 1.pdf441.5 kBAdobe PDFView/Open
chapter 2.pdf322.86 kBAdobe PDFView/Open
chapter 3.pdf709.8 kBAdobe PDFView/Open
chapter 4.pdf705.34 kBAdobe PDFView/Open
chapter 5.pdf748.97 kBAdobe PDFView/Open
chapter 6.pdf250.63 kBAdobe PDFView/Open
curriculum vitae.pdf246.1 kBAdobe PDFView/Open
declaration.pdf356.68 kBAdobe PDFView/Open
list of publications.pdf251.33 kBAdobe PDFView/Open
preliminary pages.pdf300.9 kBAdobe PDFView/Open
references.pdf476.53 kBAdobe PDFView/Open
title.pdf276.58 kBAdobe PDFView/Open


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