International Journal of Information and Communication Technology Research
مجله بین المللی ارتباطات و فناوری اطلاعات
International Journal of Information and Communication Technology Research
Engineering & Technology
http://ijict.itrc.ac.ir
1
admin
2251-6107
2783-4425
doi
1652
25391
en
jalali
1390
3
1
gregorian
2011
6
1
3
2
online
1
fulltext
fa
Customizing Feature Decision Fusion Model using Information Gain, Chi-Square and Ordered Weighted Averaging for Text Classification
فناوری اطلاعات
Information Technology
پژوهشي
Research
<p>Automatic classification of text data has been one of important research topics during recent decades. In this research, a new model based on data fusion techniques is introduced which is used for improving text classification effectiveness. This model has two major components, namely feature fusion and decision fusion; therefore, it is called Feature Decision Fusion (FDF) model. In the feature fusion component, two well-known text feature selection algorithms, Chi-Square (X<sup>2</sup>) and Information Gain (IG) were used; this component applied Ordered Weighted Averaging (OWA) operator in order to make better feature selection. The second component, Decision fusion component, combined two kinds of results using the Majority Voting (MV) algorithm. The results were obtained with feature fusion and without feature fusion. To evaluate the proposed model, K-Nearest Neighbor (KNN), Decision Tree and Perceptron Neural Network algorithms were used for classifying Rueters-21578 dataset documents. Experiments showed that this model can improve effectiveness of text classification in accordance to both Microaveraged F1 and Macro-averaged F1 measures.</p>
Text classification, text categorization, document classification, document categorization, text feature selection, data fusion
35
45
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-188&slc_lang=fa&sid=1
Mohammad Ali
Ghaderi
1003194753284600617
1003194753284600617
Yes
Behzad
Moshiri
1003194753284600618
1003194753284600618
No
Nasser
Yazdani
1003194753284600619
1003194753284600619
No
Maryam
Tayefeh Mahmoudi
1003194753284600620
1003194753284600620
No