Dealing with Imbalanced data sets for Human Activity Recognition using Mobile Phone sensors

Abstract : In the recent years, the wide spreading of smart-phones which are daily carried by humans and fit with tens of sensors triggered an intense research activity in human activity recognition (HAR). HAR in smartphones is seen as essential not only to better understand human behavior in daily life but also for context provision to other applications in the smartphone. Many statistical and logical based models for on-line or off-line HAR have been designed, however, the current trend is to use deep-learning with neural network. These models need a high amount of data and, as most discriminative models, they are very sensitive to the imbalanced class problem. In this paper, we study different ways to deal with imbalanced data sets to improve accuracy of HAR with neu-ral networks and introduce a new oversampling method, called Border Limited Link SMOTE (BLL SMOTE) which improves the classification accuracy of Multi-Layer Perceptron (MLP) performances.
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Communication dans un congrès
3rd International Workshop on Smart Sensing Systems, Jun 2018, Rome, Italy
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https://hal.archives-ouvertes.fr/hal-01950472
Contributeur : François Portet <>
Soumis le : lundi 10 décembre 2018 - 20:45:40
Dernière modification le : lundi 11 février 2019 - 16:36:02

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imblanced-data-human.pdf
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  • HAL Id : hal-01950472, version 1

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Ky Trung Nguyen, François Portet, Catherine Garbay. Dealing with Imbalanced data sets for Human Activity Recognition using Mobile Phone sensors. 3rd International Workshop on Smart Sensing Systems, Jun 2018, Rome, Italy. 〈hal-01950472〉

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