Classification of authors for an automatic recommendation process for criminal responsibility
One 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...
- Autores:
-
amelec, viloria
Pineda Lezama, Omar Bonerge
Chang, Eduardo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7692
- Acceso en línea:
- https://hdl.handle.net/11323/7692
https://doi.org/10.1016/j.procs.2020.07.098
https://repositorio.cuc.edu.co/
- Palabra clave:
- Authorship attribution
Classification features
Noise resistant algorithms
Feature reduction
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Classification of authors for an automatic recommendation process for criminal responsibility |
| title |
Classification of authors for an automatic recommendation process for criminal responsibility |
| spellingShingle |
Classification of authors for an automatic recommendation process for criminal responsibility Authorship attribution Classification features Noise resistant algorithms Feature reduction |
| title_short |
Classification of authors for an automatic recommendation process for criminal responsibility |
| title_full |
Classification of authors for an automatic recommendation process for criminal responsibility |
| title_fullStr |
Classification of authors for an automatic recommendation process for criminal responsibility |
| title_full_unstemmed |
Classification of authors for an automatic recommendation process for criminal responsibility |
| title_sort |
Classification of authors for an automatic recommendation process for criminal responsibility |
| dc.creator.fl_str_mv |
amelec, viloria Pineda Lezama, Omar Bonerge Chang, Eduardo |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Pineda Lezama, Omar Bonerge Chang, Eduardo |
| dc.subject.spa.fl_str_mv |
Authorship attribution Classification features Noise resistant algorithms Feature reduction |
| topic |
Authorship attribution Classification features Noise resistant algorithms Feature reduction |
| description |
One 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. |
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2020 |
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2020 |
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2021-01-15T14:14:20Z |
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2021-01-15T14:14:20Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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1877-0509 |
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https://hdl.handle.net/11323/7692 |
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https://doi.org/10.1016/j.procs.2020.07.098 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/7692 https://doi.org/10.1016/j.procs.2020.07.098 https://repositorio.cuc.edu.co/ |
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eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] Vorobeva, A. A. (2016, April). Examining the performance of classification algorithms for imbalanced data sets in web author identification. In 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT) (pp. 385-390). IEEE. [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. [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. [4] Win, K. N., Li, K., Chen, J., Viger, P. F., & Li, K. (2019). Fingerprint classification and identification algorithms for criminal investigation: A survey. Future Generation Computer Systems. [5] Tarmizi, N., Saee, S., & Ibrahim, D. H. A. (2020). Author identification for under-resourced language Kadazandusun. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), 248-255. [6] Sun, S. (2019). Application of Fuzzy Image Restoration in Criminal Investigation. Journal of Visual Communication and Image Representation, 102704. [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. [8] Watson, D. (2019). Source Code Stylometry and Authorship Attribution for Open Source (Master's thesis, University of Waterloo). [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. [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. [11] Mutanen, T. P., Metsomaa, J., Liljander, S., & Ilmoniemi, R. J. (2018). Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm. Neuroimage, 166, 135-151. [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. [13] Hasanov, A., & Mukanova, B. (2017). Fourier Collocation Algorithm for identification of a spacewise dependent source in wave equation from Neumann-type measured data. Applied Numerical Mathematics, 111, 49-63. [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. [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. |
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amelec, viloria2f22a05451ff1bbfc2d4dd00035c952fPineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bdChang, Eduardodb6350d46eafddc811976942b18049a23002021-01-15T14:14:20Z2021-01-15T14:14:20Z20201877-0509https://hdl.handle.net/11323/7692https://doi.org/10.1016/j.procs.2020.07.098Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/One 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.application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317981Authorship attributionClassification featuresNoise resistant algorithmsFeature reductionClassification of authors for an automatic recommendation process for criminal responsibilityArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Vorobeva, A. A. (2016, April). Examining the performance of classification algorithms for imbalanced data sets in web author identification. In 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT) (pp. 385-390). IEEE.[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.[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.[4] Win, K. N., Li, K., Chen, J., Viger, P. F., & Li, K. (2019). Fingerprint classification and identification algorithms for criminal investigation: A survey. Future Generation Computer Systems.[5] Tarmizi, N., Saee, S., & Ibrahim, D. H. A. (2020). Author identification for under-resourced language Kadazandusun. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), 248-255.[6] Sun, S. (2019). Application of Fuzzy Image Restoration in Criminal Investigation. Journal of Visual Communication and Image Representation, 102704.[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.[8] Watson, D. (2019). Source Code Stylometry and Authorship Attribution for Open Source (Master's thesis, University of Waterloo).[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.[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.[11] Mutanen, T. P., Metsomaa, J., Liljander, S., & Ilmoniemi, R. J. (2018). Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm. Neuroimage, 166, 135-151.[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.[13] Hasanov, A., & Mukanova, B. (2017). Fourier Collocation Algorithm for identification of a spacewise dependent source in wave equation from Neumann-type measured data. Applied Numerical Mathematics, 111, 49-63.[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.[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. 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