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

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. Data mining becomes the foundation for many different domains as one of its chores is the text categorization, which can be used in order to resolve the impediment of automatic plagiarism detection. This article is devoted to a new approach for combating plagiarism named MML (Multi-agents Machine learning system) and is 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 voting phase using three supervised learning algorithms (decision tree c4.5, naïve Bayes and support vector machine).