Name
Abstract | ||
|---|---|---|
We propose DOBRO, a light online learning module, which is equipped with a smart correction policy helping making decision to correct or not the given prediction depending on how likely the correction will lead to a better prediction performance. DOBRO is a standalone module requiring nothing more than a time series of prediction errors and it is flexible to be integrated into any black-box model to improve its performance under drifts. We performed evaluation in a real-world application with bus arrival time prediction problem. The obtained results show that DOBRO improved prediction performance significantly meanwhile it did not hurt the accuracy when drift does not happen.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2851613.2851888 | SAC 2016: Symposium on Applied Computing
Pisa
Italy
April, 2016 |
Field | DocType | ISBN |
Online learning,Mean squared prediction error,Simulation,Computer science,Autoregressive integrated moving average,Concept drift,Artificial intelligence,Robot | Conference | 978-1-4503-3739-7 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandr V. Maslov | 1 | 0 | 0.68 |
| Hoang Thanh Lam | 2 | 108 | 8.49 |
| Mykola Pechenizkiy | 3 | 1655 | 125.40 |
| Eric Bouillet | 4 | 47 | 5.06 |
| Tommi Kärkkäinen | 5 | 197 | 29.59 |