2023/06/11 by Kartheek Bondugula, Santiago Mazuelas, Bondugula, Kartheek +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Machine Learning in Bioinformatics #Metabolomics and Mass Spectrometry Studies
paper · pdf · doi:10.48550/arxiv.2306.06649
openalex publication_date 2023/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
High-dimensional data is common in multiple areas, such as health care and genomics, where the number of features can be tens of thousands. In such scenarios, the large number of features often leads to inefficient learning. Constraint generation methods have recently enabled efficient learning of L1-regularized support vector machines (SVMs). In this paper, we leverage such methods to obtain an efficient learning algorithm for the recently proposed minimax risk classifiers (MRCs). The proposed iterative algorithm also provides a sequence of worst-case error probabilities and performs feature selection. Experiments on multiple high-dimensional datasets show that the proposed algorithm is efficient in high-dimensional scenarios. In addition, the worst-case error probability provides useful information about the classifier performance, and the features selected by the algorithm are competitive with the state-of-the-art.