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Feature Selection for Classification of Kidney-Renal Failure

http://hdl.handle.net/20.500.12678/0000004138
1b5c5160-fe7e-4211-beb9-ceebfb05d6a9
28da50a5-6cc2-4ab0-a6fa-acfdcd7b6128
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55270.pdf 55270.pdf (271 Kb)
Publication type
Article
Upload type
Publication
Title
Title Feature Selection for Classification of Kidney-Renal Failure
Language en
Publication date 2009-12-30
Authors
Htun, Phyu Phyu
Htun, Moe Sanda
Description
Several recent machines learning publication demonstrates the utility of using feature selection algorithm in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention .In the feature subset selection problem , a learning algorithm is faced with problem of selecting a relevant subset of feature upon which to focus its attention to achieve the highest predictive accuracy with the learning algorithm on this domain , a feature subset selection method should consider how the algorithm and the training data interact with wrapper method .This paper is described the use of feature selection techniques that uses sequential forward selection to improve the performance of classifier and compute the performance of Naive Bayesian with complete feature set and selected feature set.
Keywords
Feature Selection, Sequential Forward Selection, Naive Bayesian Classification
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1838
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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