Name
Abstract | ||
|---|---|---|
A main focus of machine learning research has been improving the generalization accuracy and efficiency of prediction models. However, what emerges as missing in many applications is actionability, i.e., the ability to turn prediction results into actions. Existing effort in deriving such actionable knowledge is few and limited to simple action models while in many real applications those models are often more complex and harder to extract an optimal solution. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11704-017-6315-2 | Frontiers Comput. Sci. |
Keywords | Field | DocType |
actionable knowledge extraction,machine learning,planning,random forest | Data-driven,Computer science,Artificial intelligence,Predictive modelling,Random forest,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 5 | 2095-2228 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| qiang | 1 | 26 | 5.86 |
| Yixin Chen | 2 | 4326 | 299.19 |
| Zhaorong Li | 3 | 0 | 0.34 |
| Zhicheng Cui | 4 | 88 | 6.52 |
| Ling Chen | 5 | 57 | 5.96 |
| Xing Zhang | 6 | 0 | 0.68 |
| Haihua Shen | 7 | 4 | 3.81 |