%0 Journal Article
%T Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound
%A Celaya-Padilla
%A Jos¨¦ M.
%A Galv¨¢n-Tejada
%A Carlos E.
%A Galv¨¢n-Tejada
%A Jorge I.
%A Gamboa-Rosales
%A Hamurabi
%A Garc¨ªa-Dominguez
%A Antonio
%A Luna-Garc¨ªa
%A Huizilopoztli
%A Magallanes-Quintanar
%A Rafael
%A Zanella-Calzada
%A Laura A.
%J -
%D 2020
%R https://doi.org/10.1155/2020/8617430
%X In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children¡¯s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy
%U https://www.hindawi.com/journals/misy/2020/8617430/