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Activity Number:
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456
- Exploiting Lower-Dimensional Structure in Gaussian Process Regression
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Type:
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Invited
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Date/Time:
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Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #322471
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Title:
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Classification Trees for Imbalanced Data: Surface-to-Volume Regularization
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Author(s):
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Yichen Zhu* and Cheng Li and David Dunson
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Companies:
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Duke University and National University of Singapore and Duke University
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Keywords:
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CART;
Categorical data;
Decision boundary;
Shape penalization
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Abstract:
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Classification algorithms face difficulties when one or more classes have limited training data. We are particularly interested in classification trees, due to their interpretability and flexibility. When data are limited in one or more of the classes, the estimated decision boundaries are often irregularly shaped due to the limited sample size, leading to poor generalization error. We propose a novel approach that penalizes the Surface-to-Volume Ratio (SVR) of the decision set, obtaining a new class of SVR-Tree algorithms. We develop a simple and computationally efficient implementation while proving estimation consistency for SVR-Tree and rate of convergence for an idealized empirical risk minimizer of SVR-Tree. SVR-Tree is compared with multiple algorithms that are designed to deal with imbalance through real data applications.
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Authors who are presenting talks have a * after their name.
