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Feature Selection for Classification of Kidney-Renal Failure

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dc.contributor.author Htun, Phyu Phyu
dc.contributor.author Htun, Moe Sanda
dc.date.accessioned 2019-08-06T03:38:01Z
dc.date.available 2019-08-06T03:38:01Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1838
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.subject Feature Selection en_US
dc.subject Sequential Forward Selection en_US
dc.subject Naive Bayesian Classification en_US
dc.title Feature Selection for Classification of Kidney-Renal Failure en_US
dc.type Article en_US

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