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Application of mutual information-based sequential feature selection to ISBSG mixed data

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Application of mutual information-based sequential feature selection to ISBSG mixed data

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dc.contributor.author Fernández-Diego, Marta es_ES
dc.contributor.author González-Ladrón-de-Guevara, Fernando es_ES
dc.date.accessioned 2019-05-31T20:44:39Z
dc.date.available 2019-05-31T20:44:39Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0963-9314 es_ES
dc.identifier.uri http://hdl.handle.net/10251/121378
dc.description.abstract [EN] There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Software Quality Journal es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Feature selection es_ES
dc.subject Mutual information es_ES
dc.subject ISBSG es_ES
dc.subject Software development effort estimation es_ES
dc.subject K-nearest neighbor es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Application of mutual information-based sequential feature selection to ISBSG mixed data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11219-017-9391-5 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Fernández-Diego, M.; González-Ladrón-De-Guevara, F. (2018). Application of mutual information-based sequential feature selection to ISBSG mixed data. Software Quality Journal. 26(4):1299-1325. https://doi.org/10.1007/s11219-017-9391-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s11219-017-9391-5 es_ES
dc.description.upvformatpinicio 1299 es_ES
dc.description.upvformatpfin 1325 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 26 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\374638 es_ES
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