Siamak Mirzaei

Work place: College of Science and Engineering, Flinders University, South Australia



Research Interests:


Siamak Mirzaei: received his BSc degree in Computer Software Engineering from Karaj Azad University in 2011 in Iran. He completed his MS degree in Information Technology at the Flinders University of South Australia in 2016. Followed by his Master's, he completed a Graduate Diploma in Research Methods at Flinders University in 2017. He is currently a PhD candidate of the College of Science and Engineering at Flinders University. His research interests include mobile application development, serious games, technology use in education and vocabulary learning/teaching.

Author Articles
Text Classification based on Discriminative-Semantic Features and Variance of Fuzzy Similarity

By Pouyan Parsafard Hadi Veisi Niloofar Aflaki Siamak Mirzaei

DOI:, Pub. Date: 8 Apr. 2022

Due to the rapid growth of the Internet, large amounts of unlabelled textual data are producing daily. Clearly, finding the subject of a text document is a primary source of information in the text processing applications. In this paper, a text classification method is presented and evaluated for Persian and English. The proposed technique utilizes variance of fuzzy similarity besides discriminative and semantic feature selection methods. Discriminative features are those that distinguish categories with higher power and the concept of semantic feature takes into the calculations the similarity between features and documents by using only available documents. In the proposed method, incorporating fuzzy weighting as a measure of similarity is presented. The fuzzy weights are derived from the concept of fuzzy similarity which is defined as the variance of membership values of a document to all categories in the way that with some membership value at the same time, the sum of these membership values should be equal to 1. The proposed document classification method is evaluated on three datasets (one Persian and two English datasets) and two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Comparing the results with other text classification methods, demonstrate the consistent superiority of the proposed technique in all cases. The weighted average F-measure of our method are %82 and %97.8 in the classification of Persian and English documents, respectively.

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