dc.creator | García Vallejo, Carlos Antonio | es |
dc.creator | Troyano Jiménez, José Antonio | es |
dc.creator | Enríquez de Salamanca Ros, Fernando | es |
dc.creator | Ortega Rodríguez, Francisco Javier | es |
dc.creator | Cruz Mata, Fermín | es |
dc.date.accessioned | 2021-02-03T11:17:09Z | |
dc.date.available | 2021-02-03T11:17:09Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | García Vallejo, C.A., Troyano Jiménez, J.A., Enríquez de Salamanca Ros, F., Ortega Rodríguez, F.J. y Cruz Mata, F. (2020). MCFS: Min-cut-based feature-selection. Knowledge-Based Systems, 195 (May 2020, 105604) | |
dc.identifier.issn | 0950-7051 | es |
dc.identifier.uri | https://hdl.handle.net/11441/104525 | |
dc.description.abstract | In this paper, MCFS (Min-Cut-based feature-selection) is presented, which is a feature-selection algorithm based on the representation of the features in a dataset by means of a directed graph. The main contribution of our work is to show the usefulness of a general graph-processing technique in the feature-selection problem for classification datasets. The vertices of the graphs used herein are the features together with two special-purpose vertices (one of which denotes high correlation to the feature class of the dataset, and the other denotes a low correlation to the feature class). The edges are functions of the correlations among the features and also between the features and the classes. A classic max-flow min-cut algorithm is applied to this graph. The cut returned by this algorithm provides the selected features. We have compared the results of our proposal with well-known feature-selection techniques. Our algorithm obtains results statistically similar to those achieved by the other techniques in terms of number of features selected, while additionally significantly improving the accuracy. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades RTI2018-098 062-A-I00 | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-82113-C2-1-R | es |
dc.format | application/pdf | es |
dc.format.extent | 9 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Knowledge-Based Systems, 195 (May 2020, 105604) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine learning | es |
dc.subject | Feature selection | es |
dc.subject | Nearest neighbour | es |
dc.subject | Correlations | es |
dc.subject | Max-flow min-cut | es |
dc.subject | Classification | es |
dc.title | MCFS: Min-cut-based feature-selection | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-098 062-A-I00 | es |
dc.relation.projectID | TIN2017-82113-C2-1-R | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0950705120300757 | es |
dc.identifier.doi | 10.1016/j.knosys.2020.105604 | es |
dc.journaltitle | Knowledge-Based Systems | es |
dc.publication.volumen | 195 | es |
dc.publication.issue | May 2020, 105604 | es |
dc.identifier.sisius | 21918029 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |