Name
Abstract | ||
|---|---|---|
In classification tasks, an object usually belongs to one class within a set of disjoint classes. In more complex tasks, an object can belong to more than one class, in what is conventionally termed multi-label classification. Moreover, there are cases in which the set of classes are organised in a hierarchical fashion, and an object must be associated to a single path in this hierarchy, defining the so-called hierarchical classification. Finally, in even more complex scenarios, the classes are organised in a hierarchical structure and the object can be associated to multiple paths of this hierarchy, defining the problem investigated in this article: hierarchical multi-label classification (HMC). We address a typical problem of HMC, which is protein function prediction, and for that we propose an approach that chains multiple neural networks, performing both local and global optimisation in order to provide the final prediction: one or multiple paths in the hierarchy of classes. We experiment with four variations of this chaining process, and we compare these strategies with the state-of-the-art HMC algorithms for protein function prediction, showing that our novel approach significantly outperforms these methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3019612.3019664 | SAC |
Field | DocType | Citations |
Chaining,Disjoint sets,Computer science,Multi-label classification,Artificial intelligence,Artificial neural network,Hierarchy,Protein function prediction | Conference | 2 |
PageRank | References | Authors |
0.37 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonatas Wehrmann | 1 | 30 | 5.42 |
| Rodrigo C. Barros | 2 | 448 | 32.54 |
| Silvia N. das Dôres | 3 | 2 | 0.37 |
| Ricardo Cerri | 4 | 132 | 16.88 |