.png){kind=icon}
Return
Learning CP-Nets Structure From Preference Data Streams
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2873087.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the sharp increase of digital data emerging at present, the data in new applications are generated fast. Continuous cumulative data have gradually become massive and difficult to be handled due to limited workspace and limited amount of time. The conventional learning conditional preference networks' algorithm cannot successfully process the data streams. In this paper, we introduce the model of learning CP-nets from preference data streams and formalize the question. Then, an incremental approach is presented through which we can learn the CP-nets with gradually increasing data streams. The proposed method is verified on simulated data and real data, and it is also compared with other works.
Keywords:
Preference learning
dynamic CP-net
data streams
incremental approach
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSave
View more