Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- AutoML; Intelligent binning; Regularized regression; Binary classification; Data preparation (1)
- COVID-19 modeling; ARIMA time series; Data analysis; Gender differences; Forecasting accuracy (1)
- Change point detection; Support vector methods; Online anomaly detection; Sequential data streams; Kernel functions (1)
- Graph neural networks; Interpretability; Efficiency; Anomaly detection; Sampling strategies (1)
Articles 1 - 4 of 4
Full-Text Articles in Categorical Data Analysis
Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu
Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu
Electronic Theses and Dissertations, 2020-2023
Automated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists' works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning. One limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to …
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Electronic Theses and Dissertations, 2020-2023
Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation …
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Electronic Theses and Dissertations, 2020-2023
Sequential multiple change point detection concerns the identification of multiple points in time where the systematic behavior of a statistical process changes. A special case of this problem, called online anomaly detection, occurs when the goal is to detect the first change and then signal an alert to an analyst for further investigation. This dissertation concerns the use of methods based on kernel functions and support vectors to detect changes. A variety of support vector-based methods are considered, but the primary focus concerns Least Squares Support Vector Data Description (LS-SVDD). LS-SVDD constructs a hypersphere in a kernel space to bound …
An Evaluation Of The Performance Of Proc Arima's Identify Statement: A Data-Driven Approach Using Covid-19 Cases And Deaths In Florida, Fahmida Akter Shahela
An Evaluation Of The Performance Of Proc Arima's Identify Statement: A Data-Driven Approach Using Covid-19 Cases And Deaths In Florida, Fahmida Akter Shahela
Electronic Theses and Dissertations, 2020-2023
Understanding data on novel coronavirus (COVID-19) pandemic, and modeling such data over time are crucial for decision making at managing, fighting, and controlling the spread of this emerging disease. This thesis work looks at some aspects of exploratory analysis and modeling of COVID-19 data obtained from the Florida Department of Health (FDOH). In particular, the present work is devoted to data collection, preparation, description, and modeling of COVID-19 cases and deaths reported by FDOH between March 12, 2020, and April 30, 2021. For modeling data on both cases and deaths, this thesis utilized an autoregressive integrated moving average (ARIMA) times …