dc.creator | Silva, Jesús | |
dc.creator | H, H | |
dc.creator | Núñez, Vladimir | |
dc.creator | Ruiz Lázaro, Alex | |
dc.creator | Varela Izquierdo, Noel | |
dc.date | 2020-04-17T00:16:16Z | |
dc.date | 2020-04-17T00:16:16Z | |
dc.date | 2020-02-01 | |
dc.date.accessioned | 2023-10-03T20:04:18Z | |
dc.date.available | 2023-10-03T20:04:18Z | |
dc.identifier | 17426588 | |
dc.identifier | https://hdl.handle.net/11323/6212 | |
dc.identifier | 10.1088/1742-6596/1432/1/012074 | |
dc.identifier | Corporación Universidad de la Costa | |
dc.identifier | REDICUC - Repositorio CUC | |
dc.identifier | https://repositorio.cuc.edu.co/ | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9174196 | |
dc.description | This study describes a model of explanations in natural language for classification
decision trees. The explanations include global aspects of the classifier and local aspects of the
classification of a particular instance. The proposal is implemented in the ExpliClas open source
Web service [1], which in its current version operates on trees built with Weka and data sets with
numerical attributes. The feasibility of the proposal is illustrated with two example cases, where
the detailed explanation of the respective classification trees is shown. | |
dc.description | Este estudio describe un modelo de explicaciones en lenguaje natural para la clasificación.
árboles de decisión. Las explicaciones incluyen aspectos globales del clasificador y aspectos locales del
clasificación de una instancia particular. La propuesta se implementa en el código abierto ExpliClas
Servicio web [1], que en su versión actual opera en árboles construidos con Weka y conjuntos de datos con
atributos numéricos La viabilidad de la propuesta se ilustra con dos casos de ejemplo, donde
Se muestra la explicación detallada de los respectivos árboles de clasificación. | |
dc.format | application/pdf | |
dc.format | application/pdf | |
dc.language | eng | |
dc.publisher | Journal of Physics: Conference Series | |
dc.relation | [2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data."
International Journal of Computer Applications 101.1 (2014): 19-24. | |
dc.relation | [3] S. Ramiırez-Gallego, A. Fernandez, S. Garcıa, M. Chen, and F. Herrera, “Big data: Tutorial and
guidelines on information and process fusion for analytics algorithms with mapreduce,”
Information Fusion, vol. 42, pp. 51 – 61, 2018 | |
dc.relation | [4] M. Hamstra, H. Karau, M. Zaharia, A. Konwinski, and P. Wendell, Learning Spark: LightningFast Big Data Analytics. O’Reilly Media, 2015. | |
dc.relation | [5] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., PortilloMedina R. (2018) Measures of Concentration and Stability: Two Pedagogical Tools for Industrial
Organization Courses. In: Tan Y., Shi Y., Tang Q. (eds) Advances in Swarm Intelligence. ICSI
2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham | |
dc.relation | [6] J. Lin, “Mapreduce is good enough? if all you have is a hammer, throw away everything that’s
not a nail!” Big Data, vol. 1, no. 1, pp. 28–37, 2013. | |
dc.relation | [7] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer
Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science
And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371. | |
dc.relation | [8] D. Garcia-Gil, S. Ramiırez-Gallego, S. Garcia, and F. Herrera, “Principal Components Analysis
Random Discretization Ensemble for Big Data,” Knowledge-Based Systems, vol. 150, pp. 166 –
174, 2018. | |
dc.relation | [9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A
Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. | |
dc.relation | [10] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J.
(2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the
Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y.,
Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol
10943. Springer, Cham. | |
dc.relation | [11] L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell, “Generating
visual explanations,” in Proceedings of the European Conference on Computer Vision (ECCV),
Amsterdam, The Netherlands, 2016, pp. 3–19. | |
dc.relation | [12] A. Gatt and E. Krahmer, “Survey of the state of the art in natural language generation: Core tasks,
applications and evaluation,” Journal of Artificial Intelligence Research, vol. 61, pp. 65–170,
2018. | |
dc.relation | [13] Ruß G. Data Mining of Agricultural Yield Data: A Comparison of Regression Models, In: Perner
P. (eds) Advances in Data Mining. Applications and Theoretical Aspects, ICDM 2009. Lecture
Notes in Computer Science, vol 5633. | |
dc.relation | [14] S. Barocas and D. Boyd, “Computing ethics. engaging the ethics of data science in practice,”
Communications of the ACM, vol. 60, no. 11, pp. 23–25, 2017. | |
dc.relation | [15] Hernández, J. A., Burlak, G., Muñoz Arteaga, J., y Ochoa, A. (2006). Propuesta para la evaluación
de objetos de aprendizaje desde una perspectiva integral usando minería de datos. En A.
Hernández y J. Zechinelli (Eds.), Avances en la ciencia de la computación (pp. 382-387). México:
Universidad Autónoma de México. | |
dc.relation | [16] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A
Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. | |
dc.relation | [16] S. Gang Wu, F. Sheng Bao, E. You Xu, Y.-X. Wang, Y.-F. Chang, and Q.-L. Xiang, “A leaf
recognition algorithm for plant classification using probabilistic neural network,” in IEEE
International Symposium on Signal Processing and Information Technology, 2007, pp. 1–6.. | |
dc.relation | [17] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools
and Techniques, 4th ed. Morgan Kaufmann, 2016. | |
dc.relation | [18] D. Gunning, “Explainable Artificial Intelligence (XAI),” Defense Advan- ced Research Projects
Agency (DARPA), Arlington, USA, Tech. Rep., 2016, DARPA-BAA-16-53 | |
dc.relation | [19] Scheffer, T. (2004). Finding Association Rules that Trade Support Optimally Against Confidence.
Intelligent Data Analysis, 9(4), 381-395. | |
dc.relation | [20] J. M. Alonso, A. Ramos-Soto, E. Reiter, and K. van Deemter, “An exploratory study on the
benefits of using natural language for ex- plaining fuzzy rule-based systems,” in IEEE
International Conferen- ce on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 2017, pp. 1–6,
http://dx.doi.org/10.1109/FUZZ-IEEE.2017.8015489. | |
dc.relation | [21] M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M. J. Franklin, S. Shenker,
and I. Stoica, “Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster
computing,” in Procee- dins of the 9th USENIX Symposium on Networked Systems Design and
Implementation (NSDI 12). San Jose, CA: USENIX, 2012, pp. 15–28 | |
dc.relation | [22] S. Verbaeten and A. Assche, “Ensemble methods for noise elimination in classification
problems,” in 4th International Workshop on Multiple Classifier Systems, ser. Lecture Notes on
Computer Science, vol. 2709. Springer, 2003, pp. 317–325. | |
dc.rights | CC0 1.0 Universal | |
dc.rights | http://creativecommons.org/publicdomain/zero/1.0/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | http://purl.org/coar/access_right/c_abf2 | |
dc.subject | Modelo de explicaciones | |
dc.subject | Arboles de decisión | |
dc.subject | Código abierto ExpliClas | |
dc.subject | Explanation model | |
dc.subject | Decision trees | |
dc.subject | Open source ExpliClas | |
dc.title | Natural language explanation model for decision trees | |
dc.type | Artículo de revista | |
dc.type | http://purl.org/coar/resource_type/c_6501 | |
dc.type | Text | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/submittedVersion | |
dc.type | http://purl.org/redcol/resource_type/ART | |
dc.type | info:eu-repo/semantics/acceptedVersion | |
dc.type | http://purl.org/coar/version/c_ab4af688f83e57aa | |