A comparative study on the effect of feature selection on classification accuracy

被引：62
|
作者：
Karabulut, Esra Mahsereci [1 ]
Ozel, Selma Ayse [1 ]
Ibrikci, Turgay [1 ]
机构：
[1] Gaziantep Univ, Gaziantep Vocat High Sch, TR-27310 Gaziantep, Turkey
来源：
FIRST WORLD CONFERENCE ON INNOVATION AND COMPUTER SCIENCES (INSODE 2011) | 2012年 / 1卷
关键词：
feature selection; classification; accuracy; Naive Bayes; multilayer perceptron;
D O I：
10.1016/j.protcy.2012.02.068
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Feature selection has become interest to many research areas which deal with machine learning and data mining, because it provides the classifiers to be fast, cost-effective, and more accurate. In this paper the effect of feature selection on the accuracy of NaiveBayes, Artificial Neural Network as Multilayer Perceptron, and J48 decision tree classifiers is presented. These classifiers are compared with fifteen real datasets which are pre-processed with feature selection methods. Up to 15.55% improvement in classification accuracy is observed, and Multilayer Perceptron appears to be the most sensitive classifier to feature selection.
引用
收藏
页码：323 / 327
页数：5
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