Particle Swarm Model Selection for Authorship Verification

摘要

Authorship verification is the task of determining whether documents were or were not written by a certain author. The problem has been faced by using binary classifiers, one per author, that make individual yeso decisions about the authorship condition of documents. Traditionally, the same learning algorithm is used when building the classifiers of the considered authors. However, the individual problems that such classifiers face are different for distinct authors, thus using a single algorithm may lead to unsatisfactory results. This paper describes the application of particle swarm model selection (PSMS) to the problem of authorship verification. PSMS selects an ad-hoc classifier for each author in a fully automatic way; additionally, PSMS also chooses preprocessing and feature selection methods. Experimental results on two collections give evidence that classifiers selected with PSMS are advantageous over selecting the same classifier for all of the authors involved.