Name
Abstract | ||
|---|---|---|
Nowadays research has shown that including context-awareness, in a classic data mining (CDM) process can improve the overall results. The current work investigates the impact of context completeness and accuracy over predictive forecasting for soil moisture in a context-aware data mining (CADM) system. Experiments with different levels of noise and missing data in the context were performed using several machine learning algorithms for both CDM and CADM scenarios. The results show that the soil moisture prediction results are improved when using CADM, even if the quality standards are not completely met. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/01969722.2020.1798642 | CYBERNETICS AND SYSTEMS |
Keywords | DocType | Volume |
Context-aware data mining,data context quality,machine learning,soil moisture predictions | Journal | 51.0 |
Issue | ISSN | Citations |
SP7 | 0196-9722 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anca Avram | 1 | 1 | 1.03 |
| Oliviu Matei | 2 | 43 | 11.15 |
| Camelia-Mihaela Pintea | 3 | 102 | 16.15 |
| Petrica C. Pop | 4 | 183 | 27.86 |