Natural language explanation model for decision trees

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], wh...

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Autores:: Silva, Jesús
H, H
Núñez, Vladimir
Ruiz Lázaro, Alex
Varela Izquierdo, Noel

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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repository_id_str
dc.title.spa.fl_str_mv	Natural language explanation model for decision trees
title	Natural language explanation model for decision trees
spellingShingle	Natural language explanation model for decision trees Modelo de explicaciones Arboles de decisión Código abierto ExpliClas Explanation model Decision trees Open source ExpliClas
title_short	Natural language explanation model for decision trees
title_full	Natural language explanation model for decision trees
title_fullStr	Natural language explanation model for decision trees
title_full_unstemmed	Natural language explanation model for decision trees
title_sort	Natural language explanation model for decision trees
dc.creator.fl_str_mv	Silva, Jesús H, H Núñez, Vladimir Ruiz Lázaro, Alex Varela Izquierdo, Noel
dc.contributor.author.spa.fl_str_mv	Silva, Jesús H, H Núñez, Vladimir Ruiz Lázaro, Alex Varela Izquierdo, Noel
dc.subject.spa.fl_str_mv	Modelo de explicaciones Arboles de decisión Código abierto ExpliClas Explanation model Decision trees Open source ExpliClas
topic	Modelo de explicaciones Arboles de decisión Código abierto ExpliClas Explanation model Decision trees Open source ExpliClas
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.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-04-17T00:16:16Z
dc.date.available.none.fl_str_mv	2020-04-17T00:16:16Z
dc.date.issued.none.fl_str_mv	2020-02-01
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.spa.fl_str_mv	10.1088/1742-6596/1432/1/012074
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
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identifier_str_mv	17426588 10.1088/1742-6596/1432/1/012074 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/6212 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24. [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 [4] M. Hamstra, H. Karau, M. Zaharia, A. Konwinski, and P. Wendell, Learning Spark: LightningFast Big Data Analytics. O’Reilly Media, 2015. [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 [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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.. [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. [18] D. Gunning, “Explainable Artificial Intelligence (XAI),” Defense Advan- ced Research Projects Agency (DARPA), Arlington, USA, Tech. Rep., 2016, DARPA-BAA-16-53 [19] Scheffer, T. (2004). Finding Association Rules that Trade Support Optimally Against Confidence. Intelligent Data Analysis, 9(4), 381-395. [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. [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 [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.
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spelling	Silva, JesúsH, HNúñez, VladimirRuiz Lázaro, AlexVarela Izquierdo, Noel2020-04-17T00:16:16Z2020-04-17T00:16:16Z2020-02-0117426588https://hdl.handle.net/11323/621210.1088/1742-6596/1432/1/012074Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.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.Silva, JesúsHernandez Palma, Hugo Gaspar-will be generated-orcid-0000-0002-3873-0530-600Núñez, Vladimir-will be generated-orcid-0000-0003-3087-0341-600Ruiz Lázaro, Alex-will be generated-orcid-0000-0002-5974-2864-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600engJournal of Physics: Conference SeriesCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Modelo de explicacionesArboles de decisiónCódigo abierto ExpliClasExplanation modelDecision treesOpen source ExpliClasNatural language explanation model for decision treesArtículo 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[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.[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[4] M. Hamstra, H. Karau, M. Zaharia, A. Konwinski, and P. Wendell, Learning Spark: LightningFast Big Data Analytics. O’Reilly Media, 2015.[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[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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..[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.[18] D. Gunning, “Explainable Artificial Intelligence (XAI),” Defense Advan- ced Research Projects Agency (DARPA), Arlington, USA, Tech. Rep., 2016, DARPA-BAA-16-53[19] Scheffer, T. (2004). Finding Association Rules that Trade Support Optimally Against Confidence. Intelligent Data Analysis, 9(4), 381-395.[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.[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[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.PublicationORIGINALNatural Language Explanation Model for Decision Trees.pdfNatural Language Explanation Model for Decision Trees.pdfapplication/pdf762999https://repositorio.cuc.edu.co/bitstreams/5a0032cc-51b0-44d9-80e2-be716cd14ad8/download54acb86f7e942c479b263d5badb94bcdMD51Natural Language Explanation Model for Decision Trees.pdfNatural Language Explanation Model for Decision Trees.pdfapplication/pdf1489967https://repositorio.cuc.edu.co/bitstreams/36e7da5d-5020-422b-8fb1-4281a3981a80/download4c0531c19f9816d55c5f999c60c9d0ccMD55CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/fc69133a-88ce-4139-8da2-e3f181509a9b/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/ce4c4159-9c11-4533-bcff-b5a7c98b4cfe/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILNatural Language Explanation Model for Decision Trees.pdf.jpgNatural Language Explanation Model for Decision Trees.pdf.jpgimage/jpeg31661https://repositorio.cuc.edu.co/bitstreams/485b527e-1fd0-464d-b477-9d6fbeafc052/download10cb8eed26abaffba9fa16f508b3812eMD54TEXTNatural Language Explanation Model for Decision Trees.pdf.txtNatural Language Explanation Model for Decision Trees.pdf.txttext/plain21870https://repositorio.cuc.edu.co/bitstreams/ce4ac125-e40e-441e-b83a-8889a54112a8/download0370392fb5f4102601b11cd7728dc3ebMD5611323/6212oai:repositorio.cuc.edu.co:11323/62122024-09-17 14:22:17.508http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Natural language explanation model for decision trees

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