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Multi-Agents Machine Learning (MML) System for Plagiarism Detection

Multi-Agents Machine Learning (MML) System for Plagiarism Detection

Hadj Ahmed Bouarara
Copyright: © 2018 |Pages: 17
ISBN13: 9781522530046|ISBN10: 1522530045|EISBN13: 9781522530053
DOI: 10.4018/978-1-5225-3004-6.ch007
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MLA

Bouarara, Hadj Ahmed. "Multi-Agents Machine Learning (MML) System for Plagiarism Detection." Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, IGI Global, 2018, pp. 103-119. https://doi.org/10.4018/978-1-5225-3004-6.ch007

APA

Bouarara, H. A. (2018). Multi-Agents Machine Learning (MML) System for Plagiarism Detection. In R. Hamou (Ed.), Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management (pp. 103-119). IGI Global. https://doi.org/10.4018/978-1-5225-3004-6.ch007

Chicago

Bouarara, Hadj Ahmed. "Multi-Agents Machine Learning (MML) System for Plagiarism Detection." In Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, 103-119. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3004-6.ch007

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Abstract

Day after day the cases of plagiarism increase and become a crucial problem in the modern world caused by the quantity of textual information available in the web. As data mining becomes the foundation for many different domains, one of its chores is a text categorization that can be used in order to resolve the impediment of automatic plagiarism detection. This chapter is devoted to a new approach for combating plagiarism named MML (Multi-agents Machine Learning system) composed of three modules: data preparation and digitalization, using n-gram character or bag of words as methods for the text representation, TF*IDF as weighting to calculate the importance of each term in the corpus in order to transform each document to a vector, and learning and vote phase using three supervised learning algorithms (decision tree c4.5, naïve Bayes, and support vector machine).

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