The assessment of feature selection methods on agglutinative language for spam email detection: A special case for Turkish

Author

Ergin, Semih ; Isik, Sinan

Author_Institution

Dept. of Electr. & Electron. Eng., Eskisehir Osmangazi Univ., Eskisehir, Turkey

fYear

2014

fDate

23-25 June 2014

Firstpage

122

Lastpage

125

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.

Keywords

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; Accuracy; Artificial neural networks; Decision trees; Electronic mail; Feature extraction; Support vector machine classification; Text categorization; Turkish; chi square; feature selection; gini index; information gain; junk; spam; unsolicited e-mail;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on

Conference_Location

Alberobello

Print_ISBN

978-1-4799-3019-7

Type

conf

DOI

10.1109/INISTA.2014.6873607

Filename

6873607