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

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

8

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252736

Filename

6252736