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Decision Making and Classification for Time Series Data

Yang, Qiwei
http://rave.ohiolink.edu/etdc/view?acc_num=osu1641385348020531

Abstract Details

2022, Doctor of Philosophy, Ohio State University, 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 requirements and reliability problems have hindered the universal adoption of the GMA in the newborn period. We used a two-step approach to design a clinically feasible and accurate CS GMA detector to address this challenge. First, we recorded 300 hospitalized infants moving on a pressure sensor mat and standard video. Masked observers with advanced GMA training classified and timed each movement and their overall impression of the pattern on the videos. The sensor mat allowed data collection with time, spatial, and pressure coordinates. Second, sensor data were treated as time series imaging data. Each time frame was treated as a single image, and features were extracted using transfer learning techniques based on image feature extraction frameworks. Feature sequences were passed through deep-learning sequential-data prediction models.
Rajiv Ramnath (Advisor)
Ping Zhang (Committee Member)
Theodore Allen (Advisor)
65 p.
Artificial Intelligence; Bioinformatics; Business Costs; Computer Engineering; Computer Science
decision making; state identification; deep learning; transfer learning; classification; time series; cyber vulnerability; cramped synchronized movement

Recommended Citations

Citations

  • Yang, Q. (2022). Decision Making and Classification for Time Series Data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1641385348020531

    APA Style (7th edition)

  • Yang, Qiwei. Decision Making and Classification for Time Series Data. 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1641385348020531.

    MLA Style (8th edition)

  • Yang, Qiwei. "Decision Making and Classification for Time Series Data." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1641385348020531

    Chicago Manual of Style (17th edition)

osu1641385348020531
326
© 2021, some rights reserved.Creative Commons License Decision Making and Classification for Time Series Data by Qiwei Yang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.