{"data":{"id":"10.6084/m9.figshare.4508729.v2","type":"dois","attributes":{"doi":"10.6084/m9.figshare.4508729.v2","prefix":"10.6084","suffix":"m9.figshare.4508729.v2","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"LUIS MIRALLES","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Multi-objective evolutionary feature selection for online sales forecasting"}],"publisher":"figshare","container":{},"publicationYear":2016,"subjects":[{"subject":"Computer Engineering"},{"subject":"80602 Computer-Human Interaction","subjectScheme":"FOR"},{"subject":"FOS: Psychology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Psychology","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Applied Computer Science"},{"subject":"80303 Computer System Security","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Computer Software"},{"subject":"91003 Machine Tools","subjectScheme":"FOR"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"170203 Knowledge Representation and Machine Learning","subjectScheme":"FOR"},{"subject":"80109 Pattern Recognition and Data Mining","subjectScheme":"FOR"},{"subject":"100602 Input, Output and Data Devices","subjectScheme":"FOR"}],"contributors":[],"dates":[{"date":"2016-12-31","dateType":"Created"},{"date":"2016-12-31","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.4508729","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["0 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Sales forecasting uses historical sales figures, in association with products characteristics and peculiarities, to predict short-term or long-term future performance in a business, and it can be used to derive sound financial and business plans. By using publicly available data, we build an accurate regression model for online sales forecasting obtained via a novel feature selection methodology composed by the application of the multi-objective evolutionary algorithm ENORA (Evolutionary NOn-dominated Radial slots based Algorithm) as search strategy in a wrapper method driven by the well-known regression model learner Random Forest. Our proposal integrates feature selection for regression, model evaluation, and decision making, in order to choose the most satisfactory model according to an \u003cem\u003ea posteriori\u003c/em\u003e process in a multi-objective context. We test and compare the performances of ENORA as multi-objective evolutionary search strategy against a standard multi-objective evolutionary search strategy such as NSGA-II (Non-dominated Sorted Genetic Algorithm), against a classical backward search strategy such as RFE (Recursive Feature Elimination), and against the original data set.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/articles/dataset/Multi-objective_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