A Variable Selection Analysis for Soundscape Emotion Modeling Using Decision Tree Regression and Modern Information Criteria

dc.contributor.authorSan Millán-Castillo, Roberto
dc.contributor.authorMartino, Luca
dc.contributor.authorMorgado, Eduardo
dc.date.accessioned2025-02-21T11:49:07Z
dc.date.available2025-02-21T11:49:07Z
dc.date.issued2024-07-03
dc.description.abstractDuring the last decade, soundscape research has become one of the most active topics in Acoustics. This work provides a nonlinear variable selection analysis over the well-known dataset ‘emo-soundscapes’. Namely, we provide a selection of the soundscape indicators using a nonlinear and nonparametric model as a regression tree method. Modern techniques (proposed in the literature) have been used, first for ranking the variables and then for choosing the effective number of features. We have also compared and discussed our results with those provided previously in the literature. This study, based on modern techniques in selecting the effective number of variables, confirms the result presented in previous recent work (but based on a linear model) that very parsimonious models should be considered (in the case of a nonlinear model, it is based on very few variables, from 2 to 4, depending on the output). All the results are obtained by analyzing a single dataset. As future research works, we plan to extend our study by also considering alternative datasets.
dc.identifier.citationR. S. Millán-Castillo, L. Martino and E. Morgado, "A Variable Selection Analysis for Soundscape Emotion Modeling Using Decision Tree Regression and Modern Information Criteria," in IEEE Access, vol. 12, pp. 92622-92634, 2024, doi: 10.1109/ACCESS.2024.3422832
dc.identifier.doi10.1109/ACCESS.2024.3422832
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10115/78018
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSoundscape emotion recognition
dc.subjectdecision tree regression
dc.subjectranking methods
dc.subjectvariable selection
dc.titleA Variable Selection Analysis for Soundscape Emotion Modeling Using Decision Tree Regression and Modern Information Criteria
dc.typeArticle

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