Feature Selection with Complexity Measure in a Quadratic Programming Setting

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Ricardo Sousa (Autor) Outra; Helder P Oliveira (Autor) FEUP; Jaime S Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: PATTERN RECOGNITION AND IMAGE ANALYSIS: 5TH IBERIAN CONFERENCE, IBPRIA 2011

Páginas: 524-531

5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

Las Palmas, SPAIN, JUN 08-10, 2011

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-002-WW4

DOI: 10.1007/978-3-642-21257-4_65

Abstract (EN): Feature selection is a topic of growing interest mainly due to the increasing amount of information, being an essential task in many machine learning problems with high dimensional data. The selection of a subset of relevant features help to reduce the complexity of the problem and the building of robust learning models. This work presents an adaptation of a recent quadratic programming feature selection technique that identifies in one-fold the redundancy and relevance on data. Our approach introduces a non-probabilistic measure to capture the relevance based on Minimum Spanning Trees. Three different real datasets were used to assess the performance of the adaptation. The results are encouraging and reflect the utility of feature selection algorithms.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8