Doctor of Philosophy, The Ohio State University, 2022, Computer Science and Engineering
With the continuous increase of time series data, more and more research is focused on
using these data to improve people's lives. On the one hand, the Markov Decision Process
(MDP) is used widely in decision-making. An agent can decide the best action based on its
current state. When the agent is applied to time series data, the model will help people make
more informed decisions. However, state identification, which is very important in obtaining
an optimal decision, has received less attention. On the other hand, with the development of
deep learning, identifying the category of a time series has become more and more precise.
As a result, the recognition of complex time series sequences has become the hub of public
attention. In this dissertation, we focus on developing an automatic state selection using
MDP and investigate the application of deep learning in recognizing time series data.
We propose a method that combines decision-tree modeling and MDP to permit automatic
state identification in a way that offers desirable trade-offs between simplicity and
Markovian behavior. We first create a simplified definition of the host state, which becomes
the response measure in our decision-tree model. Then, we fit the model in a way that
weighs accuracy and interpretability. The leaves of the resulting decision-tree model become
the system states. This follows, intuitively, because these are the groupings needed to predict
(approximately) the system evolution. Then, we generate and apply an MDP control policy.
Our motivating example is cyber vulnerability maintenance. Using the proposed methods, we predict that a Midwest university could save more than four million dollars compared to
the current policy.
Prechtl's general movements assessment (GMA) allows visual recognition of movement
patterns in infants that, when abnormal (cramped synchronized, or CS), have very high
specificity in predicting later neuromotor disorders. However, training req (open full item for complete abstract)
Committee: Rajiv Ramnath (Advisor); Ping Zhang (Committee Member); Theodore Allen (Advisor)
Subjects: Artificial Intelligence; Bioinformatics; Business Costs; Computer Engineering; Computer Science