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Cost-Sensitive Trees for Energy-Efficient Context Recognition
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DOI：10.1109/IE.2019.00011.png)
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Abstract
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An important problem in the field of context recognition is preserving the battery life of the sensing device. In this work we introduce cost-sensitive learning that takes into account the cost of having the sensors active and tries to balance it against the classification error. The benefits can be amplified by adapting the classifier to each context. This approach was tested on two real-life datasets where we achieved 78% and 80% reduction in energy consumption in exchange for almost no accuracy loss.
Keywords:
context recognition
cost-sensitive learning
decision tree
Markov chains
multi-objective optimization
battery
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