Weighted Voting and Meta-Learning for Combining Authorship Attribution Methods
Pages 328 - 335
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.
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Information
Published In
Nov 2018
889 pages
ISBN:978-3-030-03492-4
DOI:10.1007/978-3-030-03493-1
© Springer Nature Switzerland AG 2018.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 21 November 2018
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