Name
Abstract | ||
|---|---|---|
Big data processing is a challenging job. Extensive time-series data need a method of preparation, management, and feature calculation for each data arrival. FIMT-DD is an algorithm for processing predictive regression for big data. The splitting criteria in the standard FIMT-DD algorithm use a Hoeffding Bound. We propose to change the splitting criteria to Chernoff bound. The experimental results and the performance comparisons that we did have better results than the standard method. We use three real-world datasets. The improvement that we propose can produce a 2.3% accuracy improvement for traffic demand data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IWBIS50925.2020.9255607 | 2020 International Workshop on Big Data and Information Security (IWBIS) |
Keywords | DocType | ISBN |
Intelligent Systems,Data Stream,Chernoff Bound,Standard Deviation,FIMT-DD,Big Data | Conference | 978-1-7281-9099-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petrus Mursanto | 1 | 17 | 3.72 |
| Ari Wibisono | 2 | 15 | 5.68 |
| Wendy D. W. T. Bayu | 3 | 0 | 0.34 |
| Valian Fil Ahli | 4 | 0 | 0.34 |
| May Iffah Rizki | 5 | 0 | 0.34 |
| Lintang Matahari Hasani | 6 | 0 | 0.34 |
| Jihan Adibah | 7 | 0 | 0.68 |