MACRO-CLASS SELECTION FOR HIERARCHICAL K-NN CLASSIFICATION OF INERTIAL SENSOR DATA

Corey McCall, Kishore Reddy, Mubarak Shah

2012

Abstract

Quality classifiers can be difficult to implement on the limited resources of an embedded system, especially if the data contains many confusing classes. This can be overcome by using a hierarchical set of classifiers in which specialized feature sets are used at each node to distinguish within the macro-classes defined by the hierarchy. This method exploits the fact that similar classes according to one feature set may be dissimilar according to another, allowing normally confused classes to be grouped and handled separately. However, determining these macro-classes of similarity is not straightforward when the selected feature set has yet to be determined. In this paper, we present a new greedy forward selection algorithm to simultaneously determine good macro-classes and the features that best distinguish them. The algorithm is tested on two human activity recognition datasets: CMU-MMAC (29 classes), and a custom dataset collected from a commodity smartphone for this paper (9 classes). In both datasets, we employ statistical features obtained from on-body IMU sensors. Classification accuracy using the selected macro-classes was increased 69% and 12% respectively over our non-hierarchical baselines.

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Paper Citation


in Harvard Style

McCall C., Reddy K. and Shah M. (2012). MACRO-CLASS SELECTION FOR HIERARCHICAL K-NN CLASSIFICATION OF INERTIAL SENSOR DATA . In Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-8565-00-6, pages 106-114. DOI: 10.5220/0003819101060114


in Bibtex Style

@conference{peccs12,
author={Corey McCall and Kishore Reddy and Mubarak Shah},
title={MACRO-CLASS SELECTION FOR HIERARCHICAL K-NN CLASSIFICATION OF INERTIAL SENSOR DATA},
booktitle={Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2012},
pages={106-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003819101060114},
isbn={978-989-8565-00-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - MACRO-CLASS SELECTION FOR HIERARCHICAL K-NN CLASSIFICATION OF INERTIAL SENSOR DATA
SN - 978-989-8565-00-6
AU - McCall C.
AU - Reddy K.
AU - Shah M.
PY - 2012
SP - 106
EP - 114
DO - 10.5220/0003819101060114