000128182 001__ 128182
000128182 005__ 20231127104407.0
000128182 0247_ $$2doi$$a10.1007/s00521-018-3938-7
000128182 0248_ $$2sideral$$a110402
000128182 037__ $$aART-2019-110402
000128182 041__ $$aeng
000128182 100__ $$0(orcid)0000-0002-2726-6760$$aGarcía-Magariño, I.
000128182 245__ $$aEstimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods
000128182 260__ $$c2019
000128182 5060_ $$aAccess copy available to the general public$$fUnrestricted
000128182 5203_ $$aThe opacity of real-estate market involves some challenges in their agent-based simulation. While some real-estate Web sites provide the prices of a great amount of houses publicly, the prices of the rest are not available. The estimation of these prices is necessary for simulating their evolution from a complete initial set of houses. Additionally, this estimation could also be useful for other purposes such as appraising houses, letting buyers know which are the best offered prices (i.e., the lowest ones compared to the appraisals) and recommending the buyers to set an initial price. This work proposes combining dimensionality reduction methods with machine learning techniques to obtain the estimated prices. In particular, this work analyzes the use of nonnegative factorization, recursive feature elimination and feature selection with a variance threshold, as dimensionality reduction methods. It compares the application of linear regression, support vector regression, the k-nearest neighbors and a multilayer perceptron neural network, as machine learning techniques. This work has applied a tenfold cross-validation for comparing the estimations and errors and assessing the improvement over a basic estimator commonly used in the beginning of simulations. The developed software and the used dataset are freely available from a data research repository for the sake of reproducibility and the support to other researchers.
000128182 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T81$$9info:eu-repo/grantAgreement/ES/MEC/CAS17-00005$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/MTM2015-65433-P$$9info:eu-repo/grantAgreement/ES/UZ/IT1-18$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2017-TEC-03$$9info:eu-repo/grantAgreement/ES/UZ/PIIDUZ-16-120
000128182 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000128182 590__ $$a4.774$$b2019
000128182 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b23 / 136 = 0.169$$c2019$$dQ1$$eT1
000128182 592__ $$a0.796$$b2019
000128182 593__ $$aSoftware$$c2019$$dQ1
000128182 593__ $$aArtificial Intelligence$$c2019$$dQ2
000128182 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000128182 700__ $$0(orcid)0000-0001-7671-7540$$aMedrano, C.$$uUniversidad de Zaragoza
000128182 700__ $$0(orcid)0000-0003-2156-9856$$aDelgado, J.$$uUniversidad de Zaragoza
000128182 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000128182 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000128182 773__ $$g32 (2019), 2665 - 2682$$pNeural comput. appl.$$tNeural Computing and Applications$$x0941-0643
000128182 8564_ $$s1135615$$uhttps://zaguan.unizar.es/record/128182/files/texto_completo.pdf$$yPostprint
000128182 8564_ $$s1488489$$uhttps://zaguan.unizar.es/record/128182/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000128182 909CO $$ooai:zaguan.unizar.es:128182$$particulos$$pdriver
000128182 951__ $$a2023-11-27-09:26:25
000128182 980__ $$aARTICLE