| Full text | |
| Author(s): |
Colombini, Gustavo G.
;
de Abreu, Iuri Bonna M.
;
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 | |
In Machine Learning, multi-label classification is the task of assigning an instance to two or more categories simultaneously. This is a very challenging task, since datasets can have many instances and become very unbalanced. While most of the methods in the literature use supervised learning to solve multilabel problems, in this paper we propose the use of unsupervised learning through neural networks. More specifically, we explore the power of Self-Organizing Maps (Kohonen Maps), since they have a self-organization ability and maps input instances to a map of neurons. Because instances that are assigned to similar groups of labels tend to be more similar, there is a network tendency that, after organization, training instances which are similar to each other are mapped to closer neurons in the map. Testing instances can then be mapped to specific neurons in the network, being classified in the labels assigned to training instances mapped to these neurons. Our proposal was experimentally compared to other literature methods, showing competitive performances. The evaluation was performed using freely available datasets and 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 |