Browsing by Subject "Open-set classification"

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    Open Set Authorship Attribution Toward Demystifying Victorian Periodicals
    (Springer, 2021-09) Badirli, Sarkhan; Borgo Ton, Mary; Gungor, Abdulmecit; Dundar, Murat; Computer and Information Science, School of Science
    Existing research in computational authorship attribution (AA) has primarily focused on attribution tasks with a limited number of authors in a closed-set configuration. This restricted set-up is far from being realistic in dealing with highly entangled real-world AA tasks that involve a large number of candidate authors for attribution during test time. In this paper, we study AA in historical texts using a new data set compiled from the Victorian literature. We investigate the predictive capacity of most common English words in distinguishing writings of most prominent Victorian novelists. We challenged the closed-set classification assumption and discussed the limitations of standard machine learning techniques in dealing with the open set AA task. Our experiments suggest that a linear classifier can achieve near perfect attribution accuracy under closed set assumption yet, the need for more robust approaches becomes evident once a large candidate pool has to be considered in the open-set classification setting.