Paper Info
Title
The assessment of feature selection methods on agglutinative language for spam email detection: A special case for Turkish
Abstract
In this study, the assessment of three different feature selection methods including Information Gain (IG), Gini Index (GI), and CHI square (CHI2) is made by utilizing two popular pattern classifiers, namely Artificial Neural Network (ANN) and Decision Tree (DT), on the classification of Turkish e-mails. The feature vectors are constructed by the bag-of-words feature extraction method. This paper is focused on the Turkish language since it is one of the widely used agglutinative languages all around the world. The results obviously reveal that CHI2 and GI feature selection methods are more efficacious than IG method for Turkish language.
Year
DOI
Venue
2014
10.1109/INISTA.2014.6873607
Innovations in Intelligent Systems and Applications
Keywords
Field
DocType
decision trees,natural language processing,neural nets,pattern classification,statistical analysis,unsolicited e-mail,ann classifiers,chi square,dt classifier,gini index,ig,turkish e-mail classification,turkish language,agglutinative language,artificial neural network,bag-of-words feature extraction method,decision tree,electronic mail,feature selection methods,information gain,spam email detection,turkish,feature selection,junk,spam,artificial neural networks,accuracy,feature extraction
Turkish,Feature selection,Computer science,Agglutinative language,Speech recognition,Special case
Conference
ISBN
Citations 
PageRank 
978-1-4799-3019-7
0
0.34
References 
Authors
7
2
Name
Order
Citations
PageRank
Semih Ergin1272.12
Sahin Isik243.42