Hierarchical multi-label classification with chained neural networks
| Autor: | Ricardo Cerri, Rodrigo C. Barros, Silvia N. das Dôres, Jonatas Wehrmann |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Multi-label classification Theoretical computer science Artificial neural network Hierarchy (mathematics) Computer science business.industry 02 engineering and technology Object (computer science) Class (biology) Set (abstract data type) 03 medical and health sciences 030104 developmental biology Chaining 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Protein function prediction Artificial intelligence business |
| Zdroj: | SAC |
| DOI: | 10.1145/3019612.3019664 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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