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Feature Selection for Classification of Kidney-Renal Failure
http://hdl.handle.net/20.500.12678/0000004138
http://hdl.handle.net/20.500.12678/00000041381b5c5160-fe7e-4211-beb9-ceebfb05d6a9
28da50a5-6cc2-4ab0-a6fa-acfdcd7b6128
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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 |