Exploiting Domain Structure with Hybrid Generative-Discriminative Models
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Date
2020-02-27
Authors
Kelly, Austen
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Journal ISSN
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Publisher
University of Oregon
Abstract
Machine learning methods often face a tradeoff between the accuracy of discriminative models and the lower sample complexity of their generative counterparts. This inspires a need for hybrid methods. In this paper we present the graphical ensemble classifier (GEC), a novel combination of logistic regression and naive Bayes. By partitioning the feature space based on known independence structure, GEC is able to handle datasets with a diverse set of features and achieve higher accuracy than a purely discriminative model from less training data. In addition to describing the theoretical basis of our model, we show the practical effectiveness on artificial data, along with the 20-newsgroups, MNIST, and MediFor datasets.
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Keywords
machine learning, probabilistic graphical models