Feature Selection for Classification of Kidney-Renal Failure

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.