Eliminating Sycophants to Improve Authorship Attribution

Classification problem in authorship attribution consists of choosing the correct author of a document from an exhaustive list of candidates presented by the samples of their writing. A typical approach is to assign a vector representing measurements of a stylometric feature to each sample document and apply a supervised machine learning method to build a classifier. Different classifiers vary in the accuracy and attributions of the disputed documents. In our previous research, we have shown that a large number of classifiers can be combined into an effective jury via weighted voting. Such a jury is almost always more accurate than individual classifiers. In this paper, we investigate whether it is possible to improve a jury’s accuracy by eliminating some of its members. We test and compare two methods of reduction. Dynamic reduction selects a subset of original jury members by eliminating sycophants. Static reduction tests the behavior of preselected juries. Our testbed is a collection of 18th-century political writings, a fertile research ground rich with disputed works.