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The disputed federalist papers: SVM feature selection via concave minimization

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Glenn FungAuthors Info & Claims

TAPIA '03: Proceedings of the 2003 conference on Diversity in computing

Pages 42 - 46

https://doi.org/10.1145/948542.948551

Published: 15 October 2003 Publication History

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Abstract

In this paper, we use a method proposed by Bradley and Mangasarian "Feature Selection via Concave Minimization and Support Vector Machines" to solve the well-known disputed Federalist Papers classification problem. We find a separating plane that classifies correctly all the "training set" papers of known authorship, based on the relative frequencies of only three words. Using the obtained separating hyperplane in three dimensions, all of the 12 disputed papers ended up on the Madison side of the separating plane. This result coincides with previous work on this problem using other classification techniques.

References

[1]

K. P. Bennett and O. L. Mangasarian. Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1:23--34, 1992.

Crossref

Google Scholar

[2]

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Occam's razor. Information Processing Letters, 24:377--380, 1987.

Digital Library

Google Scholar

[3]

R. A. Bosch and J. A. Smith. Separating hyperplanes and the authorship of the disputed federalist papers. American Mathematical Monthly, 105(7):601--608, August-September 1998.

Crossref

Google Scholar

[4]

P. S. Bradley and O. L. Mangasarian. Feature selection via concave minimization and support vector machines. In J. Shavlik, editor, Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), pages 82--90, San Francisco, California, 1998. Morgan Kaufmann. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-03.ps.

Digital Library

Google Scholar

[5]

P. S. Bradley, O. L. Mangasarian, and J. B. Rosen. Parsimonious least norm approximation. Computational Optimization and Applications, 11(1):5--21, October 1998. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/97-03.ps.

Digital Library

Google Scholar

[6]

P. S. Bradley, O. L. Mangasarian, and W. N. Street. Feature selection via mathematical programming. INFORMS Journal on Computing, 10(2):209--217, 1998. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/95-21.ps.

Digital Library

Google Scholar

[7]

V. Cherkassky and F. Mulier. Learning from Data - Concepts, Theory and Methods. John Wiley & Sons, New York, 1998.

Digital Library

Google Scholar

[8]

O. L. Mangasarian. Machine learning via polyhedral concave minimization. In H. Fischer, B. Riedmueller, and S. Schaeffler, editors, Applied Mathematics and Parallel Computing - Festschrift for Klaus Ritter, pages 175--188. Physica-Verlag A Springer-Verlag Company, Heidelberg, 1996. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/95-20.ps.

Crossref

Google Scholar

[9]

O. L. Mangasarian. Arbitrary-norm separating plane. Operations Research Letters, 24:15--23, 1999. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/97-07r.ps.

Digital Library

Google Scholar

[10]

O. L. Mangasarian. Generalized support vector machines. In A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 135--146, Cambridge, MA, 2000. MIT Press. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-14.ps.

Google Scholar

[11]

T. M. Mitchell. Machine Learning. McGraw-Hill, Boston, 1997.

Digital Library

Google Scholar

[12]

F. Mosteller and D. L. Wallace. Inference and Disputed Authorship: The Federalist. Addison-Wesley, Massachusetts, series in behavioral science:quantitative methods edition, 1964.

Google Scholar

[13]

V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.

Digital Library

Google Scholar

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Mei YHan WLi SWu XLin KQi Y(2022)A Review of Attribution Technical for APT Attacks2022 7th IEEE International Conference on Data Science in Cyberspace (DSC)10.1109/DSC55868.2022.00077(512-518)Online publication date: Jul-2022
https://doi.org/10.1109/DSC55868.2022.00077
Telli MBentobache MMokhtari A(2020)A successive linear approximation algorithm for the global minimization of a concave quadratic programComputational and Applied Mathematics10.1007/s40314-020-01317-139:4Online publication date: 12-Sep-2020
https://doi.org/10.1007/s40314-020-01317-1
AYDEMİR E(2019)Türkçe Köşe Yazılarında Yapay Sinir Ağlarıyla Yazar ve Gazete Tahmin EtmeDÜMF Mühendislik Dergisi10.24012/dumf.42575410:1(45-56)Online publication date: 15-Mar-2019
https://doi.org/10.24012/dumf.425754
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The disputed federalist papers: SVM feature selection via concave minimization

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Published In

TAPIA '03: Proceedings of the 2003 conference on Diversity in computing

October 2003

82 pages

ISBN:1581137907

DOI:10.1145/948542

Conference Chairs:
Juan C. Meza
Lawrence Berkeley National Laboratory
,
Bryant York
Portland State University
,
Program Chairs:
Monica L. Martinez-Canales
Sandia National Laboratories
,
Pamela J. Williams
Sandia National Laboratories

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Association for Computing Machinery

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Published: 15 October 2003

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TAPIA03: Richard Tapia Celebration of Diversity in Computing Conference

October 15 - 18, 2003

Georgia, Atlanta, USA

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Mei YHan WLi SWu XLin KQi Y(2022)A Review of Attribution Technical for APT Attacks2022 7th IEEE International Conference on Data Science in Cyberspace (DSC)10.1109/DSC55868.2022.00077(512-518)Online publication date: Jul-2022
https://doi.org/10.1109/DSC55868.2022.00077
Telli MBentobache MMokhtari A(2020)A successive linear approximation algorithm for the global minimization of a concave quadratic programComputational and Applied Mathematics10.1007/s40314-020-01317-139:4Online publication date: 12-Sep-2020
https://doi.org/10.1007/s40314-020-01317-1
AYDEMİR E(2019)Türkçe Köşe Yazılarında Yapay Sinir Ağlarıyla Yazar ve Gazete Tahmin EtmeDÜMF Mühendislik Dergisi10.24012/dumf.42575410:1(45-56)Online publication date: 15-Mar-2019
https://doi.org/10.24012/dumf.425754
Banga RBhardwaj APeng SShrivastava G(2018)Authorship Attribution for Online Social MediaSocial Network Analytics for Contemporary Business Organizations10.4018/978-1-5225-5097-6.ch008(141-165)Online publication date: 2018
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Rocha AScheirer WForstall CCavalcante TTheophilo AShen BCarvalho AStamatatos E(2017)Authorship Attribution for Social Media ForensicsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2016.260396012:1(5-33)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TIFS.2016.2603960
Pearl LLu KHaghighi A(2016) The Character in the Letter: Epistolary Attribution in Samuel Richardson’s Clarissa Digital Scholarship in the Humanities10.1093/llc/fqw007(fqw007)Online publication date: 18-Mar-2016
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Fung GMangasarian O(2016)Unsupervised and Semisupervised Classification Via Absolute Value InequalitiesJournal of Optimization Theory and Applications10.1007/s10957-015-0818-5168:2(551-558)Online publication date: 1-Feb-2016
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U. AThampi S(2015)Authorship AnalysisNew Threats and Countermeasures in Digital Crime and Cyber Terrorism10.4018/978-1-4666-8345-7.ch010(173-194)Online publication date: 2015
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Savoy JBouthillier FJu B(2013)The federalist papers revisitedProceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries10.5555/2655780.2655807(1-8)Online publication date: 1-Nov-2013
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Text classification based on data partitioning and parameter varying ensembles

Extending twin support vector machine classifier for multi-category classification problems

All-in-one multicategory least squares nonparallel hyperplanes support vector machine

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