Sigmis: A Feature Selection Algorithm Using Correlation Based Method

被引：42

作者：

Blessie, E. Chandra ^{[1
]}

Karthikeyan, E. ^{[2
]}

机构：

[1] DJ Acad Managerial Excellence, Dept Comp Sci, Coimbatore 641032, Tamil Nadu, India

[2] Govt Arts Coll, Dept Comp Sci, Udumalpet, Tamil Nadu, India

来源：

JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY | 2012年 / 6卷 / 03期

关键词：

Feature selection; Dimensionality; Correlation and missing data;

D O I：

10.1260/1748-3018.6.3.385

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Feature Selection is one of the preprocessing steps in machine learning tasks. Feature Selection is effective in reducing the dimensionality, removing irrelevant and redundant feature. In this paper, we propose a new feature selection algorithm (Sigmis) based on Correlation method for handling the continuous features and the missing data. Empirical comparison with three existing feature selection algorithms using UCI data sets shows that the proposed system is very effective and efficient in selecting the feature set.

引用

页码：385 / 394

页数：10

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