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dc.contributor.authorPonce, Hiram
dc.contributor.authorMartinez-Villaseñor, Lourdes
dc.contributor.otherCampus Ciudad de Méxicoes
dc.coverage.spatialMéxico
dc.creatorHIRAM EREDIN PONCE ESPINOSA;376768
dc.creatorMARÍA DE LOURDES GUADALUPE MARTÍNEZ VILLASEÑOR;241561
dc.date.accessioned2018-03-02T15:10:16Z
dc.date.available2018-03-02T15:10:16Z
dc.date.issued2017
dc.identifier.citationPonce, H. y Martinez-Villaseñor, L. (2017). Interpretability of artificial hydrocarbon networks for breast cancer classification. En: IJCNN 2017 : proceedings of the International Joint Conference on Neural Networks, (2017-may), (pp. 3535-3542). Piscataway, NJ : Institute of Electrical and Electronics Engineers. DOI: http://dx.doi.org/10.1109/IJCNN.2017.7966301en
dc.identifier.isbn9781509061815
dc.identifier.isbn9781509061839
dc.identifier.urihttps://hdl.handle.net/20.500.12552/4478
dc.identifier.urihttp://dx.doi.org/10.1109/IJCNN.2017.7966301
dc.description.abstractIn machine learning, interpretability refers to understand the underlying behavior of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to increase the usability of the method. Also, it is relevant in decision support systems, such as in medical applications. White-box models like tree-based, rule-based and linear models are considered the most comprehensible, but less accurate or simplistic. In contrast, black-box models like nonlinear and ensemble models are more accurate hence more complex to interpret. Thus, a trade-off between accuracy and interpretability is often made when building models to support human experts in a decision-making process. Artificial hydrocarbon networks (AHN) is a supervised learning method that has been proved to be very effective for regression and classification problems. In fact, its training process suggests a kind of interpretability. Thus, the objective of this work is to present first efforts proving the capacity of artificial hydrocarbon networks (AHN) to deliver interpretable models. In order to assess the interpretability of AHN, we address the breast cancer problem using a public dataset. Results showed that AHN can be transformed in treebased and rule-based models preserving high accuracy in the output classification. © 2017 IEEE.en
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofOPENAIREes
dc.relation.ispartofseries2017-May;en
dc.rightsAcceso Cerradoes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceIJCNN 2017 : proceedings of the International Joint Conference on Neural Networksen
dc.subjectArtificial intelligenceen
dc.subjectDecision makingen
dc.subjectDecision support systemsen
dc.subjectDiagnosisen
dc.subjectDiseasesen
dc.subjectEconomic and social effectsen
dc.subjectHydrocarbonsen
dc.subjectSupervised learningen
dc.subjectBreast cancer classificationsen
dc.subjectDecision making processen
dc.subjectDiagnosis criteriaen
dc.subjectInterpretabilityen
dc.subjectRule-based modelsen
dc.subjectSupervised learning methodsen
dc.subjectTraining processen
dc.subjectWhite-box modelsen
dc.subjectLearning systemsen
dc.subjectMedical applicationsen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.subject.classificationIngenieríaes
dc.titleInterpretability of artificial hydrocarbon networks for breast cancer classificationen
dc.typeContribución a congresoes
dcterms.audienceInvestigadoreses
dcterms.audienceEstudianteses
dcterms.audienceMaestroses
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dc.description.versionVersión aceptadaes
dc.identifier.doi10.1109/IJCNN.2017.7966301
dc.identifier.pagenumber3535-3542


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