Name
Title | ||
|---|---|---|
How Noisy and Missing Context Influences Predictions in a Practical Context-Aware Data Mining System. |
Abstract | ||
|---|---|---|
The focus of this research is finding out how different levels of context noise and missing data, affect the overall prediction results in a Context-Aware Data Mining (CADM) system for predicting soil moisture. Experiments were performed using more machine learning algorithms and varying the levels of noise and missing context data in realistic scenarios. The results show that context with missing data has a higher impact on the predictions than noise. Results comparable to the clean context baseline are obtained when the 20% threshold of noise and missing data is not exceeded. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-57802-2_3 | SOCO |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anca Avram | 1 | 0 | 0.34 |
| Oliviu Matei | 2 | 43 | 11.15 |
| Camelia-Mihaela Pintea | 3 | 102 | 16.15 |
| Petrica C. Pop | 4 | 183 | 27.86 |
| Carmen Ana Anton | 5 | 0 | 0.34 |