dc.creator | Riquelme Santos, José Cristóbal | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Toro Bonilla, Miguel | es |
dc.date.accessioned | 2016-06-28T07:56:15Z | |
dc.date.available | 2016-06-28T07:56:15Z | |
dc.date.issued | 2003 | |
dc.identifier.citation | Riquelme Santos, J.C., Aguilar Ruiz, J.S. y Toro Bonilla, M. (2003). Finding representative patterns withordered projections. Pattern Recognition, 36 (4), 1009-1018. | |
dc.identifier.issn | 0031-3203 | es |
dc.identifier.uri | http://hdl.handle.net/11441/42811 | |
dc.description.abstract | This paper presents a new approach to 2nding representative patterns for dataset editing. The algorithm patterns by ordered
projections (POP), has some interesting characteristics: important reduction of the number of instances from the dataset;
lower computational cost ( (mn log n)) with respect to other typical algorithms due to the absence of distance calculations;
conservation of the decision boundaries, especially from the point of view of the application of axis-parallel classifers. POP
works well in practice withbothcontinuous and discrete attributes. The performance of POP is analysed in two ways: percentage
of reduction and classifcation. POP has been compared to IB2, ENN and SHRINK concerning the percentage of reduction and
the computational cost. In addition, we have analysed the accuracy of k-NN and C4.5 after applying the reduction techniques.
An extensive empirical study using datasets with continuous and discrete attributes from the UCI repository shows that POP
is a valuable preprocessing method for the later application of any axis-parallel learning algorithm. | es |
dc.description.sponsorship | CICYT TIC2001-1143-C03-02 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Pattern Recognition, 36 (4), 1009-1018. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data mining | es |
dc.subject | Preprocessing techniques | es |
dc.subject | Pattern analysis | es |
dc.subject | Axis-parallel classifers | es |
dc.title | Finding representative patterns withordered projections | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | 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 | TIC2001-1143-C03-02 | es |
dc.relation.publisherversion | http://dx.doi.org/10.1016/S0031-3203(02)00119-X | |
dc.identifier.doi | 10.1016/S0031-3203(02)00119-X | es |
idus.format.extent | 10 | es |
dc.journaltitle | Pattern Recognition | es |
dc.publication.volumen | 36 | es |
dc.publication.issue | 4 | es |
dc.publication.initialPage | 1009 | es |
dc.publication.endPage | 1018 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/42811 | |
dc.contributor.funder | Comisión Interministerial de Ciencia y Tecnología (CICYT). España | |