THESIS
2018
Abstract
Temporal data analysis has been widely applied in various areas, such as bio-informatics,
outlier detection, and trajectory mining. These applications require a variety of machine
learning methods, either supervised or unsupervised. In this thesis, we study several supervised
and unsupervised learning methods suitable for our target applications. The first
application is to monitor the battery level in wireless communication devices, where we develop
a time series classification method to identify the working status of the devices. The
classifier achieves a high accuracy, but incurs heavy annotation cost in preparing training
data. To solve this problem, we propose an efficient and effective active learning method.
Specifically, we adapt the idea of shapelet discovery and select...[
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Temporal data analysis has been widely applied in various areas, such as bio-informatics,
outlier detection, and trajectory mining. These applications require a variety of machine
learning methods, either supervised or unsupervised. In this thesis, we study several supervised
and unsupervised learning methods suitable for our target applications. The first
application is to monitor the battery level in wireless communication devices, where we develop
a time series classification method to identify the working status of the devices. The
classifier achieves a high accuracy, but incurs heavy annotation cost in preparing training
data. To solve this problem, we propose an efficient and effective active learning method.
Specifically, we adapt the idea of shapelet discovery and select the training data based
on both the uncertainty and the utility of each data instance. This method outperforms
the state-of-the-art active learning methods on time series data. The second application is
to study the patterns in movement trajectories. In particular, we propose an unsupervised
method to identify trajectory patterns that match well-known team strategies in professional
basketball games. Our experimental results demonstrate the effectiveness of our proposed
method in comparison with traditional methods.
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