Name
Abstract | ||
|---|---|---|
In this short note we introduce multi-objective optimisation for feature subset selection in multi-label classification. We aim at optimise multiple multi-label loss functions simultaneously, using label powerset, binary relevance, classifier chains and calibrated label ranking as the multi-label learning methods, and decision trees and SVMs as base learners. Experiments on multi-label benchmark datasets show that the feature subset obtained through MOO performs reasonably better than the systems that make use of exhaustive feature sets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59569-6_5 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Multi-label classification,Multi-objective optimisation,Feature subset selection | Data mining,Classifier chains,Decision tree,Ranking,Pattern recognition,Feature selection,Computer science,Support vector machine,Multi-label classification,Feature (machine learning),Artificial intelligence,Linear classifier | Conference |
Volume | ISSN | Citations |
10260 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Arif Khan | 1 | 1 | 0.35 |
| Asif Ekbal | 2 | 737 | 119.31 |
| Eneldo Loza Menc ´ ia | 3 | 488 | 25.84 |
| Johannes Fürnkranz | 4 | 2476 | 222.90 |