Title :
Multi-Label Hierarchical Classification using a Competitive Neural Network for protein function prediction
Author :
Borges, Helyane Bronoski ; Nievola, Julio Cesar
Author_Institution :
UTFPR- Univ. Tecnol. Fed. do Parana, Curitiba, Brazil
Abstract :
Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents an algorithm for hierarchical classification using the global approach, called Multilabel Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested on some datasets from the bioinformatics field and its results are promising.
Keywords :
bioinformatics; neural nets; pattern classification; proteins; MHC-CNN; bioinformatics field; competitive neural network; multilabel hierarchical classification; protein function prediction; Artificial neural networks; Classification algorithms; Equations; Neurons; Prediction algorithms; Proteins; Training; Competitive Neural Network; Global Classifier; Hierarchical Classification;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252736