Author Identification Based on a Hybrid Features Set Using Machine Learning Techniques

This study explores a series of author attribution approaches that use some techniques for improving performances. Author attribution is the identification of the author of a text. It has attracted many attentions because of its relevance in a wide range of applications, including forensic investigations and plagiarism detection.  A large number of features and approaches have been applied to this task. However, there has been a lack of studies that involve multiple datasets or that use a different range of classifications. Therefore, in this work, we explore several machine learning tools, the fusion between classifiers and different types of features that have been applied on the C50 database.