A Comparison of Hierarchical Biclustering Ensemble Methods

Autor(es)

Padilha, Victor A. ; de Carvalho, Andre C. P. L. F. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2017-01-01.

Resumo

Biclustering aims at providing techniques able to detect submatrices following coherent patterns according to a pre-established criterion in a data matrix. These techniques are able to simultaneously cluster both dimensions of a data matrix (objects and features). Several real-world applications can benefit from the use of this paradigm (e.g., gene expression data analysis). However, biclustering is proven to be a NP-hard problem. For such, an alternative to provide more meaningful and robust solutions is the combination of several biclustering results by using ensemble methods. The development of ensemble methods is a relatively new issue in the biclustering literature. Thus, few methods have been proposed and, to the best of the authors' knowledge, no comparative study has been conducted so far. This paper compares two biclustering ensemble methods based on a hierarchical aggregation of previously found biclusters. Experiments are carried out with both on synthetic and real datasets. Despite the results obtained, the authors believe that there is room for further improvement of the methods for real gene expression data scenarios. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 17/02975-0 - Ensembles de resultados de bi-agrupamento
• Beneficiário: Victor Alexandre Padilha
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE