| Full text | |
| Author(s): |
de Abreu, Iuri Bonna M.
;
Mantovani, Rafael G.
;
Cerri, Ricardo
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2017-01-01. |
| Abstract | |
Multi-label classification is a machine learning task where instances can be classified into two or more labels simultaneously. In this task, there exist correlations between the instances belonging to same or similar sets of labels. This paper proposes the incorporation of instance correlations by modifying the multi-label datasets. We used the label-space to create new features, which represent these correlations. The original and modified datasets were used with different multi-label classification methods. Experiments have shown that better results can be obtained when instance correlations were incorporated in the classification tasks. All methods were evaluated with measures specifically designed for multi-label problems. (AU) | |
| FAPESP's process: | 15/14300-1 - Hierarchical classification of transposable elements using machine learning |
| Grantee: | Ricardo Cerri |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 12/23114-9 - Use of meta-learning for parameter tuning for classification problems |
| Grantee: | Rafael Gomes Mantovani |
| Support Opportunities: | Scholarships in Brazil - Doctorate |