A Parallel Computing Hybrid Approach for Feature Selection

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Silva, J (Autor) Outra; Ana Aguiar (Autor) FEUP; Silva, F (Autor) FCUP

Ata de Conferência Internacional

Título: 2015 IEEE 18th International Conference on Computational Science and Engineering

Páginas: 97-104

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 6 Citações

Outras Informações

ID Authenticus: P-00K-03X

DOI: 10.1109/cse.2015.34

Abstract (EN): The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8