A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification of Alzheimer's disease using handwriting

A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification of Alzheimer's disease using handwriting

In this paper, we introduce an efficient machine learning method based on robust Support Vector Machines (SVMs) that simultaneously classifies data and selects relevant features whilst accounting for data uncertainty. Based on Wasserstein distributionally robust optimization, we develop computationa...

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Bibliographic Details
Main Authors: Q.Y. Huang, N.D. Dizon, N. Jeyakumar, V. Jeyakumar
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:EURO Journal on Computational Optimization
Subjects:
Support vector machine
Wasserstein ambiguity
Distributionally robust optimization
Feature selection
Difference-of-convex optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S2192440625000085
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