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Sensors 2016, 16(10), 1619; doi:10.3390/s16101619

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Carretera al Lago de Guadalupe Km. 3.5, Atizapán, Edo. de México C.P. 52926, Mexico
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Author to whom correspondence should be addressed.
Academic Editors: Yun Liu, Han-Chieh Chao, Pony Chu and Wendong Xiao
Received: 30 June 2016 / Revised: 24 September 2016 / Accepted: 24 September 2016 / Published: 29 September 2016
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Abstract

This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users. View Full-Text
Keywords: behavior analysis; classifier ensemble; personal risk detection; one-class classification; wearable sensor behavior analysis; classifier ensemble; personal risk detection; one-class classification; wearable sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rodríguez, J.; Barrera-Animas, A.Y.; Trejo, L.A.; Medina-Pérez, M.A.; Monroy, R. Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data. Sensors 2016, 16, 1619.

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