.png){kind=icon}
Return
Partial classification in the belief function framework
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2021.106742.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Partial, or set-valued classification assigns instances to sets of classes, making it possible to reduce the probability of misclassification while still providing useful information. This paper reviews approaches to partial classification based on the Dempster-Shafer theory of belief functions. To define the utility of set-valued predictions, we propose to extend the utility matrix using an Ordered Weighted Average operator, allowing us to model the decision maker's attitude towards imprecision using a single parameter. Various decision criteria are analyzed comprehensively. In particular, two main strategies are distinguished: partial classification based on complete preorders among partial assignments, and partial preorders among complete assignments. Experiments with UCI and simulated Gaussian data sets show the superiority of partial classification in terms of average utility, as compared to single-class assignment and classification with rejection. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Dempster-Shafer theory
Evidence theory
Supervised classification
Decision-making
Set-valued classification
OWA operator
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
.png){kind=icon}
Coaches’ implicit associations between size and giftedness: implications for the relative age effect
PREAI
ShareSaveShareSaveShareSave
View more