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Extraction of decision rules via imprecise probabilities

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Extraction of decision rules via imprecise probabilities

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Abellán, J.; López-Maldonado, G.; Garach, L.; Castellano, JG. (2017). Extraction of decision rules via imprecise probabilities. International Journal of General Systems. 46(4):313-331. https://doi.org/10.1080/03081079.2017.1312359

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Título: Extraction of decision rules via imprecise probabilities
Autor: Abellán, J. López-Maldonado, Griselda Garach, L. Castellano, Javier G.
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería e Infraestructura de los Transportes - Departament d'Enginyeria i Infraestructura dels Transports
Fecha difusión:
Resumen:
Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via ...[+]
Palabras clave: Imprecise probabilities , Imprecise Dirichlet model , Non-parametric predictive inference model , Uncertainty measures , Decision rules , Traffic accident severity
Derechos de uso: Reserva de todos los derechos
Fuente:
International Journal of General Systems. (issn: 0308-1079 )
DOI: 10.1080/03081079.2017.1312359
Editorial:
Taylor & Francis
Versión del editor: https://doi.org/10.1080/03081079.2017.1312359
Código del Proyecto:
info:eu-repo/grantAgreement/MINECO//TEC2015-69496-R/ES/DESARROLLO DE HERRAMIENTAS EN LA MINERIA DE DATOS UTILIZANDO MODELOS BASADOS EN PROBABILIDADES IMPRECISAS. APLICACIONES EN PROBLEMAS DE TRAFICO/
Descripción: "This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on 2017, available online: https://www.tandfonline.com/doi/full/10.1080/03081079.2017.1312359"
Agradecimientos:
This work has been supported by the Spanish "Ministerio de Economia y Competitividad" [Project number TEC2015-69496-R] and FEDER funds.
Tipo: Artículo

References

Abellan, J., & Bosse, E. (2018). Drawbacks of Uncertainty Measures Based on the Pignistic Transformation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(3), 382-388. doi:10.1109/tsmc.2016.2597267

Abellán, J., & Klir, G. J. (2005). Additivity of uncertainty measures on credal sets. International Journal of General Systems, 34(6), 691-713. doi:10.1080/03081070500396915

Abellán, J., & Masegosa, A. R. (2010). An ensemble method using credal decision trees. European Journal of Operational Research, 205(1), 218-226. doi:10.1016/j.ejor.2009.12.003 [+]
Abellan, J., & Bosse, E. (2018). Drawbacks of Uncertainty Measures Based on the Pignistic Transformation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(3), 382-388. doi:10.1109/tsmc.2016.2597267

Abellán, J., & Klir, G. J. (2005). Additivity of uncertainty measures on credal sets. International Journal of General Systems, 34(6), 691-713. doi:10.1080/03081070500396915

Abellán, J., & Masegosa, A. R. (2010). An ensemble method using credal decision trees. European Journal of Operational Research, 205(1), 218-226. doi:10.1016/j.ejor.2009.12.003

(2003). International Journal of Intelligent Systems, 18(12). doi:10.1002/int.v18:12

Abellán, J., Klir, G. J., & Moral, S. (2006). Disaggregated total uncertainty measure for credal sets. International Journal of General Systems, 35(1), 29-44. doi:10.1080/03081070500473490

Abellán, J., Baker, R. M., & Coolen, F. P. A. (2011). Maximising entropy on the nonparametric predictive inference model for multinomial data. European Journal of Operational Research, 212(1), 112-122. doi:10.1016/j.ejor.2011.01.020

Abellán, J., López, G., & de Oña, J. (2013). Analysis of traffic accident severity using Decision Rules via Decision Trees. Expert Systems with Applications, 40(15), 6047-6054. doi:10.1016/j.eswa.2013.05.027

Abellán, J., Baker, R. M., Coolen, F. P. A., Crossman, R. J., & Masegosa, A. R. (2014). Classification with decision trees from a nonparametric predictive inference perspective. Computational Statistics & Data Analysis, 71, 789-802. doi:10.1016/j.csda.2013.02.009

Alkhalid, A., Amin, T., Chikalov, I., Hussain, S., Moshkov, M., & Zielosko, B. (2013). Optimization and analysis of decision trees and rules: dynamic programming approach. International Journal of General Systems, 42(6), 614-634. doi:10.1080/03081079.2013.798902

Chang, L.-Y., & Chien, J.-T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17-22. doi:10.1016/j.ssci.2012.06.017

Chang, L.-Y., & Wang, H.-W. (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019-1027. doi:10.1016/j.aap.2006.04.009

DE CAMPOS, L. M., HUETE, J. F., & MORAL, S. (1994). PROBABILITY INTERVALS: A TOOL FOR UNCERTAIN REASONING. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 02(02), 167-196. doi:10.1142/s0218488594000146

DGT. 2011b.Spanish Road Safety Strategy 2011–2020, 222 p. Madrid: Traffic General Directorate.

Dolques, X., Le Ber, F., Huchard, M., & Grac, C. (2016). Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis. International Journal of General Systems, 45(2), 187-210. doi:10.1080/03081079.2015.1072927

Gray, R. C., Quddus, M. A., & Evans, A. (2008). Injury severity analysis of accidents involving young male drivers in Great Britain. Journal of Safety Research, 39(5), 483-495. doi:10.1016/j.jsr.2008.07.003

Guo, J., & Chankong, V. (2002). Rough set-based approach to rule generation and rule induction. International Journal of General Systems, 31(6), 601-617. doi:10.1080/0308107021000034353

Huang, H., Chin, H. C., & Haque, M. M. (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis. Accident Analysis & Prevention, 40(1), 45-54. doi:10.1016/j.aap.2007.04.002

Kashani, A. T., & Mohaymany, A. S. (2011). Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science, 49(10), 1314-1320. doi:10.1016/j.ssci.2011.04.019

Li, X., & Yu, L. (2016). Decision making under various types of uncertainty. International Journal of General Systems, 45(3), 251-252. doi:10.1080/03081079.2015.1086574

Mantas, C. J., & Abellán, J. (2014). Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data. Expert Systems with Applications, 41(5), 2514-2525. doi:10.1016/j.eswa.2013.09.050

Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis & Prevention, 35(5), 683-691. doi:10.1016/s0001-4575(02)00047-7

McCartt, A. T., Mayhew, D. R., Braitman, K. A., Ferguson, S. A., & Simpson, H. M. (2009). Effects of Age and Experience on Young Driver Crashes: Review of Recent Literature. Traffic Injury Prevention, 10(3), 209-219. doi:10.1080/15389580802677807

Montella, A., Aria, M., D’Ambrosio, A., & Mauriello, F. (2011). Data-Mining Techniques for Exploratory Analysis of Pedestrian Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2237(1), 107-116. doi:10.3141/2237-12

Montella, A., Aria, M., D’Ambrosio, A., & Mauriello, F. (2012). Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accident Analysis & Prevention, 49, 58-72. doi:10.1016/j.aap.2011.04.025

De Oña, J., López, G., & Abellán, J. (2013). Extracting decision rules from police accident reports through decision trees. Accident Analysis & Prevention, 50, 1151-1160. doi:10.1016/j.aap.2012.09.006

De Oña, J., López, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis & Prevention, 51, 1-10. doi:10.1016/j.aap.2012.10.016

Pande, A., & Abdel-Aty, M. (2009). Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Safety Science, 47(1), 145-154. doi:10.1016/j.ssci.2007.12.001

Peek-Asa, C., Britton, C., Young, T., Pawlovich, M., & Falb, S. (2010). Teenage driver crash incidence and factors influencing crash injury by rurality. Journal of Safety Research, 41(6), 487-492. doi:10.1016/j.jsr.2010.10.002

Sikora, M., & Wróbel, Ł. (2013). Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms. International Journal of General Systems, 42(6), 594-613. doi:10.1080/03081079.2013.798901

Walley, P. (1996). Inferences from Multinomial Data: Learning About a Bag of Marbles. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 3-34. doi:10.1111/j.2517-6161.1996.tb02065.x

Wang, Z., & Klir, G. J. (1992). Fuzzy Measure Theory. doi:10.1007/978-1-4757-5303-5

Webb, G. I. (2007). Discovering Significant Patterns. Machine Learning, 68(1), 1-33. doi:10.1007/s10994-007-5006-x

Witten, I. H., & Frank, E. (2002). Data mining. ACM SIGMOD Record, 31(1), 76-77. doi:10.1145/507338.507355

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