Explanation of machine learning classification models with fuzzy measures: an approach to individual classification

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dc.conference.date19-21 jul 2022
dc.conference.placeIzmir, Turquía
dc.conference.titleInternational Conference on Intelligent and Fuzzy Systems, INFUS 2022
dc.contributor.authorSantos, Daniel
dc.contributor.authorGutiérrez García-Pardo, Inmaculada
dc.contributor.authorCastro Cantalejo, Javier
dc.contributor.authorGómez González, Daniel
dc.contributor.authorGuevara Gil, Juan Antonio
dc.contributor.authorEspínola Vílchez, María Rosario
dc.contributor.editorKahraman, Cengiz
dc.contributor.editorTolga, A. Cagri
dc.contributor.editorOnar, Sezi Cevik
dc.contributor.editorCebi, Selcuk
dc.contributor.editorOztaysi, Basar
dc.contributor.editorSari, Irem Ucal
dc.date.accessioned2024-05-31T12:13:53Z
dc.date.available2024-05-31T12:13:53Z
dc.date.issued2022
dc.descriptionLecture Notes in Networks and Systems. Volume 505 LNNS
dc.description.abstractAbstract: In the field of Machine Learning, there is a common point in almost all methodologies about measuring the importance of features in a model: estimating the value of a collection of them in several situations where different information sources (features) are available. To establish the value of the response feature, these techniques need to know the predictive ability of some features over others. We can distinguish two ways of performing this allocation. The first does not pay attention to the available information of known characteristics, assigning a random allocation value. The other option is to assume that the feasible values for the unknown features have to be any of the values observed in the sample (in the known part of the database), assuming that the values of the known features are correct. Despite its interest, there is a serious problem of overfitting in this approach, in situations in which there is a continuous feature: the values of a continuous feature are not likely to occur in any other, so there is a large loss of randomization (there will surely be an insignificant number of records for each possible value). In this scenario, it is probably unrealistic to assume a perfect estimation. Then, in this paper we propose a new methodology based on fuzzy measures which allows the analysis and consideration of the available information in known features, avoiding the problem of overfitting in the presence of continuous features. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG
dc.description.departmentDepto. de Estadística y Ciencia de los Datos
dc.description.facultyFac. de Estudios Estadísticos
dc.description.refereedTRUE
dc.description.sponsorshipSecretaría de Estado de Investigacion, Desarrollo e Innovacion
dc.description.statuspub
dc.identifier.citationSantos, D., Gutiérrez, I., Castro, J., Gómez, D., Guevara, J.A., Espínola, R. (2022). Explanation of Machine Learning Classification Models with Fuzzy Measures: An Approach to Individual Classification. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_7
dc.identifier.doi10.1007/978-3-031-09176-6_7
dc.identifier.essn2367-3389
dc.identifier.isbn978-303109175-9
dc.identifier.issn2367-3370
dc.identifier.officialurlhttps://doi.org/10.1007/978-3-031-09176-6_7
dc.identifier.relatedurlhttps://link.springer.com/chapter/10.1007/978-3-031-09176-6_7
dc.identifier.urihttps://hdl.handle.net/20.500.14352/104635
dc.language.isoeng
dc.page.final69
dc.page.initial62
dc.relation.projectIDPR108/20-28
dc.relation.projectIDPGC2018096509-B-I00
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsmetadata only access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.cdu004.85
dc.subject.keywordExplainable artificial intelligence
dc.subject.keywordFeatures importance
dc.subject.keywordFuzzy measures
dc.subject.keywordMachine learning
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.ucmEstadística
dc.subject.unesco1203.04 Inteligencia Artificial
dc.subject.unesco1209 Estadística
dc.titleExplanation of machine learning classification models with fuzzy measures: an approach to individual classification
dc.typeconference paper
dc.type.hasVersionVoR
dspace.entity.typePublication
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