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Feature selection (FS) is the technique of selecting a subset of relevant features for building learning models. FS algorithms typically fall into two categories: feature ranking and subset selection. Feature ranking ranks the features by a metric and eliminates all features that do not achieve an adequate score. Subset selection searches the set of possible features for the optimal subset. Many FS algorithm have been proposed. This paper presents a new FS technique which is guided by Fselector Package. The package Fselector implements a novel FS algorithm which is devoted to the feature ranking and feature subset selection of high dimensional data. This package provides functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. The R package provides a convenient interface to the algorithm. This paper investigates the effectiveness of twelve commonly used FS methods on spam data set. One of the basic popular methods involves filter which select the subset of feature as preprocessing step independent of chosen classifier, Support vector machine classifier. The algorithm is designed as a wrapper around five classification algorithms. The short description of the algorithm and performance measure of its classification is presented with the spam data set.
Dr. R. Parimala, rajamohanparimala, parimala. 1970. "A Study of Spam E-mail classification using Feature Selection package". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 7).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 152
Country: India
Subject: Global Journal of Computer Science and Technology
Authors: Dr. R. Parimala, Dr. R. Nallaswamy (PhD/Dr. count: 2)
View Count (all-time): 494
Total Views (Real + Logic): 8882
Total Downloads (simulated): 712
Publish Date: 2011 03, Sun
Monthly Totals (Real + Logic):
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