Authorship verification as a one-class classification problem

Moshe Koppel; Jonathan Schler

Authorship verification as a one-class classification problem

Moshe Koppel, Jonathan Schler

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

193 Scopus citations

Abstract

In the authorship verification problem, we are given examples of the writing of a single author and are asked to determine if given long texts were or were not written by this author. We present a new learning-based method for adducing the "depth of difference" between two example sets and offer evidence that this method solves the authorship verification problem with very high accuracy. The underlying idea is to test the rate of degradation of the accuracy of learned models as the best features are iteratively dropped from the learning process.

Original language	English
Title of host publication	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Editors	R. Greiner, D. Schuurmans
Pages	489-495
Number of pages	7
State	Published - 2004
Event	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 - Banff, Alta, Canada Duration: 4 Jul 2004 → 8 Jul 2004

Publication series

Name	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Conference

Conference	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Country/Territory	Canada
City	Banff, Alta
Period	4/07/04 → 8/07/04

Cite this

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title = "Authorship verification as a one-class classification problem",

abstract = "In the authorship verification problem, we are given examples of the writing of a single author and are asked to determine if given long texts were or were not written by this author. We present a new learning-based method for adducing the {"}depth of difference{"} between two example sets and offer evidence that this method solves the authorship verification problem with very high accuracy. The underlying idea is to test the rate of degradation of the accuracy of learned models as the best features are iteratively dropped from the learning process.",

author = "Moshe Koppel and Jonathan Schler",

year = "2004",

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editor = "R. Greiner and D. Schuurmans",

booktitle = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004",

note = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 ; Conference date: 04-07-2004 Through 08-07-2004",

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Koppel, M & Schler, J 2004, Authorship verification as a one-class classification problem. in R Greiner & D Schuurmans (eds), Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, pp. 489-495, Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, Banff, Alta, Canada, 4/07/04.

Authorship verification as a one-class classification problem. / Koppel, Moshe; Schler, Jonathan.
Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. ed. / R. Greiner; D. Schuurmans. 2004. p. 489-495 (Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Authorship verification as a one-class classification problem

Abstract

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Conference

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