Name
Abstract | ||
|---|---|---|
Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science. A causal direction inference algorithm maps the observation data into a binary value which represents either x causes y or y causes x. The nature of these algorithms makes the results unstable with the change of data points. Therefore the accuracy of the causal direction inference can be improved significantly by using parallel ensemble frameworks. In this paper, new causal direction inference algorithms based on several ways of parallel ensemble are proposed. Theoretical analyses on accuracy rates are given. Experiments are done on both of the artificial data sets and the real world data sets. The accuracy performances of the methods and their computational efficiencies in parallel computing environment are demonstrated. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jpdc.2020.12.012 | Journal of Parallel and Distributed Computing |
Keywords | DocType | Volume |
Parallel ensemble,Causal direction inference,Unstable learner | Journal | 150 |
ISSN | Citations | PageRank |
0743-7315 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulai Zhang | 1 | 5 | 2.54 |
| Wang Jiachen | 2 | 0 | 0.34 |
| Cen Gang | 3 | 0 | 0.34 |
| Guiming Luo | 4 | 69 | 28.79 |