Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

Joint Authors

Gamboa-Rosales, Hamurabi
Galván-Tejada, Carlos E.
Galván-Tejada, Jorge I.
García-Dominguez, Antonio
Zanella-Calzada, Laura A.
Celaya-Padilla, José M.
Luna-García, Huizilopoztli
Magallanes-Quintanar, Rafael

Source

Mobile Information Systems

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-02-13

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Telecommunications Engineering

Abstract EN

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.

American Psychological Association (APA)

García-Dominguez, Antonio& Galván-Tejada, Carlos E.& Zanella-Calzada, Laura A.& Gamboa-Rosales, Hamurabi& Galván-Tejada, Jorge I.& Celaya-Padilla, José M.…[et al.]. 2020. Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1192485

Modern Language Association (MLA)

Gamboa-Rosales, Hamurabi…[et al.]. Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound. Mobile Information Systems No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1192485

American Medical Association (AMA)

García-Dominguez, Antonio& Galván-Tejada, Carlos E.& Zanella-Calzada, Laura A.& Gamboa-Rosales, Hamurabi& Galván-Tejada, Jorge I.& Celaya-Padilla, José M.…[et al.]. Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1192485

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1192485