Name
Title | ||
|---|---|---|
Optimizing copious activity type classes based on classification accuracy and entropy retention |
Abstract | ||
|---|---|---|
Despite the advantages, big transport data are characterized by a considerable disadvantage as well. Personal and activity-travel information are often lacking, making it necessary to deduce this information with data mining techniques. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.future.2018.04.080 | Future Generation Computer Systems |
Keywords | DocType | Volume |
Activity type classification,(Big) Transport data annotation,Optimal set of activity types,Local search algorithm,Classification accuracy,Entropy indices | Journal | 110 |
ISSN | Citations | PageRank |
0167-739X | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wim Ectors | 1 | 8 | 3.06 |
| Sofie Reumers | 2 | 0 | 0.34 |
| Won Do Lee | 3 | 0 | 1.01 |
| Bruno Kochan | 4 | 1 | 3.74 |
| Davy Janssens | 5 | 238 | 38.08 |
| Tom Bellemans | 6 | 73 | 23.16 |
| Geert Wets | 7 | 766 | 67.59 |