Weighted Voting and Meta-Learning for Combining Authorship Attribution Methods

Abstract

Our research concentrates on ways to combine machine learning techniques for authorship attribution. Traditionally, research in authorship attribution is focused on the development of new base-classifiers (combinations of stylometric features and learning methods). A large number of base-classifiers developed for authorship attribution vary in accuracy, often proposing different authors for a disputed document. In this research, we use predictions of multiple base-classifiers as a knowledge base for learning the true author. We introduce and compare two novel methods that utilize multiple base-classifiers. In the Weighted Voting approach, each base-classifier supports an author in proportion to its accuracy in leave-one-out classification. In our Meta-Learning approach, each base-classifier is treated as a feature and methods’ predictions in leave-one-out cross-validation are used as training data from which machine learning methods produce an aggregated decision. We illustrate our results through a collection of 18th century political writings. Anonymously written essays were common during this period, leading to frequent disagreements between scholars over their attribution.