Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/49373
Title: Effective multiple criterion approach for building a rule based decision tree classifier
Researcher: Yamini C
Guide(s): Punithavalli M
Keywords: decision tree classifier
multiple criterion
Science and humanities
Upload Date: 11-Sep-2015
University: Anna University
Completed Date: 01/08/2014
Abstract: The reliability of disease classification for medical dataset has newlinebecome challenging task due to the measurement of noise and biological newlineheterogeneity among patients Most of the medical datasets have complicated newlineboundaries between attributes and classes The current classification methods newlinefind only the rules with high accuracy These methods either cover only a newlinenarrow part of the objects or require numerous attributes to explain a newlineclassification rule Although these methods are computationally effective for newlinerealizing the classifications rules there are heuristic approaches that can newlineinduce feasible rules Feature selection refers to the problem of selecting those newlineinput attributes that are most predictive for a given outcome a problem newlineencountered in many areas such as machine learning pattern recognition and newlinesignal processin newline newline
Pagination: xix,180p.
URI: http://hdl.handle.net/10603/49373
Appears in Departments:Faculty of Science and Humanities

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01_title.pdfAttached File16.22 kBAdobe PDFView/Open
02_certificate.pdf5.99 kBAdobe PDFView/Open
03_abstract.pdf13.1 kBAdobe PDFView/Open
04_acknowledgement.pdf6.58 kBAdobe PDFView/Open
05_contents.pdf27.77 kBAdobe PDFView/Open
06_chapter 1.pdf178.98 kBAdobe PDFView/Open
07_chapter 2.pdf137.99 kBAdobe PDFView/Open
08_chapter 3.pdf178.48 kBAdobe PDFView/Open
09_chapter 4.pdf415.64 kBAdobe PDFView/Open
10_chapter 5.pdf343.76 kBAdobe PDFView/Open
11_chapter 6.pdf368.04 kBAdobe PDFView/Open
12_chapter 7.pdf81.05 kBAdobe PDFView/Open
13_chapter 8.pdf28.04 kBAdobe PDFView/Open
14_references.pdf59.13 kBAdobe PDFView/Open
15_publications.pdf20.5 kBAdobe PDFView/Open


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