Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/33120
Title: Enhancing the performance of Classification algorithms through Dataset tuning feature selection and Exception handling
Researcher: Appavu alias balamurugan S
Guide(s): Rajaram R
Keywords: Bayes Theorem
Knowledge Discovery in Databases
Knowledge Discovery Process
Upload Date: 20-Jan-2015
University: Anna University
Completed Date: 01/08/2009
Abstract: This thesis presents a spectrum of novel solutions for enhancing the newlineperformance of classification tasks in Knowledge Discovery Process by newlinetaking into account redundant and inconsistent data irrelevant and redundant newlinefeatures and exceptions while performing classification tasks equal newlineprobability and information gain that can arise often in real world datasets newlineThese characteristics are very often neglected by state of the art classification newlineAlgorithms The main focus of this thesis is Knowledge Discovery in newlineDatabases KDD Four different domains related to KDD namely Dataset newlineTuning Feature Selection Handling Exceptions in Classification algorithms newlineand Classification of Threatening E mail have been investigated newlineI n real world datasets lots of redundant and conflicting data exist newlinethat affect the performance of the classification algorithms They have to be newlineremoved to increase the efficiency and the accuracy of the classifiers Dataset newlinetuning is a pre processing method used to fine tune the tuples in a dataset for newlinenoise elimination Dataset tuning is done to remove redundant data and to newlinecorrect the conflicting data newlineFeature selection is a fundamental problem in data mining to select newlinerelevant features and cast away irrelevant and redundant features based on newlinesome evaluation criteria It is well known that correlated and irrelevant newlinefeatures may degrade the performance of the classification algorithms In this newlinethesis feature selection algorithms such as Bayes Feature Selector Class newlineAssociation Rule Information Gain Feature Selector and Bayes Theorem newlineInformation Gain Feature Selector are proposed newline newline
Pagination: xxiv, 254p.
URI: http://hdl.handle.net/10603/33120
Appears in Departments:Faculty of Information and Communication Engineering

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03_abstract.pdf24.56 kBAdobe PDFView/Open
04_acknowledgement.pdf19.35 kBAdobe PDFView/Open
05_content.pdf69.78 kBAdobe PDFView/Open
06_chapter1.pdf76.54 kBAdobe PDFView/Open
07_chapter2.pdf108.18 kBAdobe PDFView/Open
08_chapter3.pdf56.85 kBAdobe PDFView/Open
09_chapter4.pdf777.75 kBAdobe PDFView/Open
10_chapter5.pdf3.62 MBAdobe PDFView/Open
11_chapter6.pdf33.32 kBAdobe PDFView/Open
12_reference.pdf64.74 kBAdobe PDFView/Open
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14_vitae.pdf18.01 kBAdobe PDFView/Open
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