Name
Abstract | ||
|---|---|---|
•Our work advances the relevance-, redundancy-, and interaction-based multi-label feature selection for managing streaming features.•To the best of our knowledge, this is the first effort that accounts for intrinsic group structures of features and handling streaming features simultaneously in multi-label learning.•OMGFS can be used to perform group- and single- feature selection, simultaneously.•We validate the superiority of our proposed algorithm via comparing with other state-of-the-art multi-label feature selection algorithms from different performance views. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2017.12.008 | Knowledge-Based Systems |
Keywords | Field | DocType |
Online feature selection,Multi-label learning,Streaming feature,Group feature selection | Data mining,Data set,Feature selection,Computer science,Group selection,Redundancy (engineering),Artificial intelligence,Empirical research,Machine learning | Journal |
Volume | ISSN | Citations |
143 | 0950-7051 | 5 |
PageRank | References | Authors |
0.38 | 42 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinghua Liu | 1 | 29 | 2.94 |
| Yaojin Lin | 2 | 470 | 23.01 |
| S. X. Wu | 3 | 116 | 15.11 |
| Chenxi Wang | 4 | 30 | 2.29 |