.png){kind=icon}
.png){kind=icon}
Return
Modelling and predicting partial orders from pairwise belief functions
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-014-1553-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, weintroduce a genericway to represent and manipulate pairwise information about partial orders (representing rankings, preferences,...) with belief functions. We provide generic and practical tools to make inferences from this pairwise information and illustrate their use on the machine learning problems that are label ranking and multi-label prediction. Our approach differs from most other quantitative approaches handling complete or partial orders, in the sense that partial orders are here considered as primary objects and not as incomplete specifications of ideal but unknown complete orders.
Keywords:
Dempster-Shafer theory
Belief functions
Paired comparisons
Partial orders
Label ranking
Multilabel classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W