期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2014
卷号：6
期号：01
页码：63-68
出版社：Engg Journals Publications
摘要：Feature Selection is an important technique for classification for reducing the dimensionality of feature space and it removes redundant, irrelevant, or noisy data. In this paper the feature are selected based on the ranking methods. (1) Information Gain (IG) attribute evaluation, (2) Gain Ratio (GR) attribute evaluation, (3) Symmetrical Uncertainty (SU) attribute evaluation. This paper evaluates the features which are derived from the 3 methods using supervised learning algorithms K-Nearest Neighbor and Na�ve Bayes. The measures used for the classifier are True Positive, False Positive, Accuracy and they compared between the algorithm for experimental results. we have taken 2 data sets Pima and Wine from UCI Repository database.
关键词：Feature Selection; Na�ve Bayes; K-Nearest Neighbor and Classification Accuracy
