Boosting Algorithm and Meta-Heuristic Based on Genetic Algorithms for Textual Plagiarism Detection

Abstract

Day after day, the plagiarism cases increase and become a crucial problem in the modern world, caused by the quantity of textual information available in the web and the development of communication means such as email service. This paper deals on the unveiling of two plagiarism detection systems: Firstly boosting system based on machine learning algorithm (decision tree C4.5 and K nearest neighbour) composed on three steps (text pre-processing, first detection, and second detection). Secondly using genetic algorithm based on an initial population generated from the dataset used a fitness function fixed and the reproduction rules (selection, crossover, and mutation). For their experimentation, the authors have used the benchmark pan 09 and a set of validation measures (precision, recall, f-measure, FNR, FPR, and entropy) with a variation in configuration of each system; They have compared their results with the performance of other approaches found in literature; Finally, the visualisation service was developed that provides a graphical vision of the results using two methods (3D cub and a cobweb) with the possibility to have a detailed and global view using the functionality of zooming and rotation. The authors' aims are to improve the quality of plagiarism detection systems and preservation of copyright.