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    An Efficient Ranking and Classification Method for Linear Functions, Kernel Functions, Decision Trees, and Ensemble Methods

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    Genre
    Thesis/Dissertation
    Date
    2020
    Author
    Glass, Jesse Miller
    Advisor
    Obradovic, Zoran
    Committee member
    Vucetic, Slobodan
    Zhang, Kai
    Airoldi, Edoardo
    Department
    Computer and Information Science
    Subject
    Artificial Intelligence
    Bipartite Ranking
    Frank-wolfe Algorithm
    Gaussian Conditional Random Fields
    Multivariate Output Regression
    Pairwise Support Vector Machine
    Structural Support Vector Machine
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2925
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2907
    Abstract
    Structural algorithms incorporate the interdependence of outputs into the prediction, the loss, or both. Frank-Wolfe optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 classification surrogate losses applied to pairs of features and outputs, resulting in improved ranking performance (area under the ROC curve, average precision, and F-1 score) at the cost of increased learning complexity. In this dissertation, it is proven that the balanced loss 0-1 SVM and the pairwise SVM have the same dual loss and the pairwise dual coefficient domain is a subdomain of the balanced loss 0-1 SVM with bias dual coefficient domain. This provides a theoretical advancement in the understanding of pairwise loss, which we exploit for the development of a novel ranking algorithm that is fast and memory efficient method with state the art ranking metric performance across eight benchmark data sets. Various practical advancements are also made in multivariate output regression. The learning time for Gaussian conditional random fields is greatly reduced and the parameter domain is expanded to enable repulsion between outputs. Last, a novel multivariate regression is presented that keeps the desirable elements of GCRF and infuses them into a local regression model that improves mean squared error and reduces learning complexity.
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