Volume 3, Issue 2 (6-2011)                   IJICTR 2011, 3(2): 35-45 | Back to browse issues page

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Ghaderi M A, Moshiri B, Yazdani N, Tayefeh Mahmoudi M. Customizing Feature Decision Fusion Model using Information Gain, Chi-Square and Ordered Weighted Averaging for Text Classification . IJICTR. 2011; 3 (2) :35-45
URL: http://ijict.itrc.ac.ir/article-1-215-en.html
1- Control & Intelligent Processing Center of Excellence, School of ECE University of Tehran Tehran, Iran
2- Knowledge Management & E-Organization Group, IT Research Faculty, Research Institute for ICT School of ECE, University of Tehran Tehran, Iran
Abstract:   (1947 Views)

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 (X2) 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.

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Type of Study: Research | Subject: Information Technology

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