{"id":{"repo_id":"duquesne","oai_identifier":"oai:dsc.duq.edu:etd-2019"},"canonical_url":"https://search.dev.ndltd.org/etd/duquesne/oai:dsc.duq.edu:etd-2019","repository":{"repo_id":"duquesne","name":"Duquesne","base_url":"https://dsc.duq.edu/do/oai/"},"display":{"title":"The Open Class Authorship Attribution Problem: A Comparison of Mixture-of-Experts Methods within the JGAAP Framework","abstract":"In this paper, we seek to describe, test, evaluate, and compare methods of open class attribution that utilize multiple unique closed class attributions in a voting framework. By applying statistical techniques to the proportion of closed class attributions indicating individual candidate authors, we seek to determine if the author is present in a set of suspected authors or not. The final answer to an open class attribution problem is either one of the authors in the set of candidate authors or \"None of the above.\" We test nine different methods of open class attribution grouped into three distinct voting paradigms. We find that the most effective method is a voting method in which each closed class attribution votes equally for its top two most likely authors. Accuracies in this method are statistically better than chance and, in total, are the best out of all nine methods.","abstract_html":"In this paper, we seek to describe, test, evaluate, and compare methods of open class attribution that utilize multiple unique closed class attributions in a voting framework. By applying statistical techniques to the proportion of closed class attributions indicating individual candidate authors, we seek to determine if the author is present in a set of suspected authors or not. The final answer to an open class attribution problem is either one of the authors in the set of candidate authors or "None of the above." We test nine different methods of open class attribution grouped into three distinct voting paradigms. We find that the most effective method is a voting method in which each closed class attribution votes equally for its top two most likely authors. Accuracies in this method are statistically better than chance and, in total, are the best out of all nine methods.","abstract_has_math":false,"creators":["Overly, James Orlo"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["Patrick Juola","John Kern","Donald Simon"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T02:10:15Z","subjects":["Authorship Attribution","JGAAP","Open Class"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/1003","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Patrick Juola","John Kern","Donald Simon"]},{"key":"dc:creator","label":"Author","values":["Overly, James Orlo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-03T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Authorship Attribution","JGAAP","Open Class"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/1003"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this paper, we seek to describe, test, evaluate, and compare methods of open class attribution that utilize multiple unique closed class attributions in a voting framework. By applying statistical techniques to the proportion of closed class attributions indicating individual candidate authors, we seek to determine if the author is present in a set of suspected authors or not. The final answer to an open class attribution problem is either one of the authors in the set of candidate authors or \"None of the above.\" We test nine different methods of open class attribution grouped into three distinct voting paradigms. We find that the most effective method is a voting method in which each closed class attribution votes equally for its top two most likely authors. Accuracies in this method are statistically better than chance and, in total, are the best out of all nine methods."]},{"key":"dc:title","label":"Title","values":["The Open Class Authorship Attribution Problem: A Comparison of Mixture-of-Experts Methods within the JGAAP Framework"]}]}],"canonical_facts":{"dc:contributor":["Patrick Juola","John Kern","Donald Simon"],"dc:creator":["Overly, James Orlo"],"dc:date.available":["2018-08-03T07:00:00Z"],"dc:description.abstract":["In this paper, we seek to describe, test, evaluate, and compare methods of open class attribution that utilize multiple unique closed class attributions in a voting framework. By applying statistical techniques to the proportion of closed class attributions indicating individual candidate authors, we seek to determine if the author is present in a set of suspected authors or not. The final answer to an open class attribution problem is either one of the authors in the set of candidate authors or \"None of the above.\" We test nine different methods of open class attribution grouped into three distinct voting paradigms. We find that the most effective method is a voting method in which each closed class attribution votes equally for its top two most likely authors. Accuracies in this method are statistically better than chance and, in total, are the best out of all nine methods."],"dc:identifier":["https://dsc.duq.edu/etd/1003"],"dc:language":["English"],"dc:subject":["Authorship Attribution","JGAAP","Open Class"],"dc:title":["The Open Class Authorship Attribution Problem: A Comparison of Mixture-of-Experts Methods within the JGAAP Framework"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:10:15Z"}