.png){kind=icon}
.png){kind=icon}
Return
Support vector decision making
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2025.106087.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The paper develops a support vector machine (SVM) for binary decision-making within a utility framework. Given an information set, a decision-maker first predicts a binary outcome and then selects a binary action based on this prediction to maximize expected utility, where the utility function can depend on the action taken, observable covariates, and the binary outcome subsequently realized. The proposed maximum utility SVM differs from the traditional SVM in four key aspects. First, as a conceptual innovation, it incorporates the optimal cutoff function as a separate and special covariate. Second, there is a sign restriction on this special covariate. Third, it accounts for the dependence of the utility-induced loss on both the covariates and the binary outcome. Finally, it allows the margin to differ across different classes of outcomes. The paper proves that the proposed method is Bayes-consistent under the maximum utility criterion and establishes a finite-sample generalization bound. A simulation study shows that the proposed method outperforms existing methods under the data-generating processes considered in the literature.
Keywords:
C14
C45
C52
C53
Decision-based binary prediction
Maximum margin decision
Maximum utility estimation
Support vector decision
Support vector machine
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W
Organization
No organization information available