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M. Gashler, C. Giraud-Carrier, and T. Martinez, “Decision tree ensemble: small heterogeneous is better than large homogeneous”, IEEE Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, pp. 900-905, 2008.

has been cited by the following article:

• TITLE: Multi-Sensor Ensemble Classifier for Activity Recognition

  AUTHORS: Lingfei Mo, Shaopeng Liu, Robert X. Gao, Patty S. Freedson

  KEYWORDS: Physical Activity Assessment;Multi-Sensor Ensemble;Support Vector Machine

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 25, 2013

  ABSTRACT: This paper presents a multi-sensor ensemble classifier (MSEC) for physical activity (PA) pattern recognition of human subjects. The MSEC, developed for a wearable multi-sensor integrate measurement system (IMS),combines multiple classifiers based on different sensor feature sets to improve the accuracy of PA type identification.Experimental evaluation of 56 subjects has shown that the MSECis more effectivein assessing activities of varying intensitiesthan the traditional homogeneous classifiers. It is able to correctly recognize 6 PA types with an accuracy of 93.50%, which is 7% higher than the non-ensemble support vector machine method. Furthermore, the MSECis effective in reducing the subject-to-subject variabilityin activity recognition.