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Using negotiable features for prescription problems

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Using negotiable features for prescription problems

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dc.contributor.author Bella Sanjuán, Antonio es_ES
dc.contributor.author Ferri Ramírez, César es_ES
dc.contributor.author Hernández Orallo, José es_ES
dc.contributor.author Ramírez Quintana, María José es_ES
dc.date.accessioned 2015-02-09T09:23:50Z
dc.date.issued 2011-02
dc.identifier.issn 0010-485X
dc.identifier.uri http://hdl.handle.net/10251/46833
dc.description.abstract Data mining is usually concerned on the construction of accurate models from data, which are usually applied to well-defined problems that can be clearly isolated and formulated independently from other problems. Although much computational effort is devoted for their training and statistical evaluation, model deployment can also represent a scientific problem, when several data mining models have to be used together, constraints appear on their application, or they have to be included in decision processes based on different rules, equations and constraints. In this paper we address the problem of combining several data mining models for objects and individuals in a common scenario, where not only we can affect decisions as the result of a change in one or more data mining models, but we have to solve several optimisation problems, such as choosing one or more inputs to get the best overall result, or readjusting probabilities after a failure. We illustrate the point in the area of customer relationship management (CRM), where we deal with the general problem of prescription between products and customers. We introduce the concept of negotiable feature, which leads to an extended taxonomy of CRM problems of greater complexity, since each new negotiable feature implies a new degree of freedom. In this context, we introduce several new problems and techniques, such as data mining model inversion (by ranging on the inputs or by changing classification problems into regression problems by function inversion), expected profit estimation and curves, global optimisation through a Monte Carlo method, and several negotiation strategies in order to solve this maximisation problem. es_ES
dc.description.sponsorship This work has been partially supported by the EU (FEDER) and the Spanish MEC/MICINN, under grant TIN 2007-68093-C02, the Spanish project "Agreement Technologies" (Consolider Ingenio CSD2007-00022) and the GVA project PROMETEO/2008/051. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation EU es_ES
dc.relation Spanish MEC/MICINN [TIN 2007-68093-C02] es_ES
dc.relation info:eu-repo/grantAgreement/MEC//CSD2007-00022/ES/Agreement Technologies/ es_ES
dc.relation info:eu-repo/grantAgreement/Generalitat Valenciana//PROMETEO08%2F2008%2F051/ES/Advances on Agreement Technologies for Computational Entities (atforce)/ es_ES
dc.relation.ispartof Computing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject CRM es_ES
dc.subject Data mining es_ES
dc.subject Function inversion problem es_ES
dc.subject Global optimisation es_ES
dc.subject Monte Carlo method es_ES
dc.subject Negotiable features es_ES
dc.subject Negotiation es_ES
dc.subject Probability estimation es_ES
dc.subject Profit maximisation es_ES
dc.subject Ranking es_ES
dc.subject MONTE CARLO es_ES
dc.subject Estimation es_ES
dc.subject Global optimization es_ES
dc.subject Optimization es_ES
dc.subject Probability es_ES
dc.subject Profitability es_ES
dc.subject Public relations es_ES
dc.subject Monte Carlo methods es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Using negotiable features for prescription problems es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/s00607-010-0129-5
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Bella Sanjuán, A.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2011). Using negotiable features for prescription problems. Computing. 91(2):135-168. https://doi.org/10.1007/s00607-010-0129-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s00607-010-0129-5 es_ES
dc.description.upvformatpinicio 135 es_ES
dc.description.upvformatpfin 168 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 91 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 201406
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
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