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dc.creatoramelec, viloria
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorChang, Eduardo
dc.date.accessioned2021-01-15T14:14:20Z
dc.date.available2021-01-15T14:14:20Z
dc.date.issued2020
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11323/7692
dc.description.abstractOne problem in classifying tasks is the handling of features that characterize classes. When the list of features is long, a noise resistant algorithm of irrelevant features can be used, or these features can be reduced. Authorship attribution is a task that assigns an anonymous text to a subject on a list of possible authors, has been widely addressed as an automatic text classification task. In it, n-grams can produce long lists of features even in small corpora. Despite this, there is a lack of research exposing the effects of using noise-resistant algorithms, reducing traits, or combining both options. This paper responds to this lack by using contributions to discussion forums related to organized crime. The results show that the classifiers evaluated, in general, benefit from feature reduction, and that, thanks to such reduction, even classical algorithms outperform state-of-the-art classifiers considered highly noise resistant.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectAuthorship attributionspa
dc.subjectClassification featuresspa
dc.subjectNoise resistant algorithmsspa
dc.subjectFeature reductionspa
dc.titleClassification of authors for an automatic recommendation process for criminal responsibilityspa
dc.typearticlespa
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dcterms.references[2] Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., ... & Stamatatos, E. (2016). Authorship attribution for social media forensics. IEEE Transactions on Information Forensics and Security, 12(1), 5-33.spa
dcterms.references[3] Rico-Sulayes, A. (2017). Reducing Vector Space Dimensionality in Automatic Classification for Authorship Attribution. Revista Científica de Ingeniería Electrónica, Automática y Comunicaciones, 38(3), 26-35.spa
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dcterms.references[7] Boenninghoff, B., Nickel, R. M., Zeiler, S., & Kolossa, D. (2019, May). Similarity learning for authorship verification in social media. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2457-2461). IEEE.spa
dcterms.references[8] Watson, D. (2019). Source Code Stylometry and Authorship Attribution for Open Source (Master's thesis, University of Waterloo).spa
dcterms.references[9] Juola, P., Milička, J., & Zemánek, P. (2018). Authorship and time attribution of Arabic texts using JGAAP. In Intelligent Natural Language Processing: Trends and Applications (pp. 325-349). Springer, Cham.spa
dcterms.references[10] Hannah-Moffat, K. (2019). Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, 23(4), 453-470.spa
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dcterms.references[12] Usha, A., & Thampi, S. M. (2017, December). Authorship Analysis of Social Media Contents Using Tone and Personality Features. In International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage (pp. 212-228). Springer, Cham.spa
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dcterms.references[14] Reddy, T. R., Vardhan, B. V., & Reddy, P. V. (2016). A survey on authorship profiling techniques. International Journal of Applied Engineering Research, 11(5), 3092-3102.spa
dcterms.references[15] Sun, F., Gu, Y., Cao, Y., Lu, Q., Bai, Y., Li, L., ... & Li, T. (2019). Novel flexible pressure sensor combining with dynamic-time-warping algorithm for handwriting identification. Sensors and Actuators A: Physical, 293, 70-76.spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920317981spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.07.098


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