A k-nearest neighbors based approach applied to more realistic activity recognition datasets

Cadenas Figueredo, J. M.; Garrido Carrera, María del Carmen; Martínez España, R.; Muñoz, A.

Publication:
A k-nearest neighbors based approach applied to more realistic activity recognition datasets

dc.contributor.author	Cadenas Figueredo, J. M.
dc.contributor.author	Garrido Carrera, María del Carmen
dc.contributor.author	Martínez España, R.
dc.contributor.author	Muñoz, A.
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2024-02-01T11:28:05Z
dc.date.available	2024-02-01T11:28:05Z
dc.date.issued	2018
dc.description	© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Journal of Ambient Intelligence and Smart Environments. To access the final edited and published work see https://doi.org/10.3233/AIS-180486	es
dc.description.abstract	Due to the latest technological advances, the current society has the possibility to store large volumes of data in the majority of the problems of the daily life. These data are useless if there is not a set of techniques available to analyze them with the objective of obtaining knowledge that facilitates the problem resolution. This paper focuses on the techniques provided by data mining as a tool for intelligent data analysis in the field of human activity recognition, specifically in the application of two techniques of data mining capable of carrying out the extraction of knowledge from data that are not as accurate and exact as desirable. This type of data reflects the true nature of the information collected on a day-to-day basis. The proposed techniques allow us to perform a preprocessing of the data by means of an instance selection that improves the computational requirements of the system response, obtaining satisfactory accuracy results. Several experiments are carried out on a real world dataset and various datasets obtained from the previous one in a synthetic way to simulate more realistic datasets that illustrate the potential of the techniques proposed.	es
dc.format	application/pdf	es
dc.format.extent	12	es
dc.identifier.citation	Journal of Ambient Intelligence and Smart Environments, 10(3). 2018
dc.identifier.doi	https://doi.org/10.3233/AIS-180486
dc.identifier.issn	1876-1364
dc.identifier.uri	http://hdl.handle.net/10201/138397
dc.language	eng	es
dc.publisher	IOS Press	es
dc.relation	This work is derived from R&D projects TIN2017-86885-R, TIN2016-81731-REDT and TIN2016- 78799-P (AEI/FEDER, UE), funded by MCIN. Type of project: National.	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Imperfect Information	es
dc.subject	Fuzzy Sets	es
dc.subject	Classification	es
dc.subject	Instance Selection	es
dc.subject	Data Mining	es
dc.subject	k-Nearest Neighbors	es
dc.title	A k-nearest neighbors based approach applied to more realistic activity recognition datasets	es
dc.type	info:eu-repo/semantics/preprint	es
dspace.entity.type	Publication	es