Arabie text classification using Learning Vector Quantization
Mohammed N. Azara, Tamer S. Fatayer, Alaa M. El-Halees · International Conference on Informatics and Systems · 2012
One of the several benefits of text classification is to automatically assign document in predefined category. Researchers using LVQ algorithm in English and Persian [1, 2] and don't be attention for Arabic language. So in our research, we used neural network approach for classify Arabic text by using Learning Vector Quantization (LVQ) algorithm. This algorithm is based on Kohonen self organizing map (SOM) that is able to organize big-size document collections according to textual similarities. Also, LVQ algorithm requires less training examples and its faster than other classification methods. We select Arabic documents from different domains. After that we select suitable preprocessing methods such as term weighting schemes, and Arabic morphological analysis (stemming and light stemming), these preprocessing prepared dataset that need for classification. Then, we compared the results obtained from different LVQ improvement versions (LVQ2.1, LVQ3, OLVQ1 and OLVQ3). The results showed that the LVQ's algorithms especially LVQ2.1 algorithm achieved high accuracy and less time compared to other LVQ's algorithms.