Title :
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
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;
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
DOI :
10.1109/INISTA.2014.6873607
