Minimizing the imbalance problem in chromatographic profile classification with one-class classifiers

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2008

Autores

Antonio V Sousa (Autor) Outra; Ana Maria Mendonca (Autor) FEUP; Aurelio Campilho (Autor) FEUP

Ata de Conferência Internacional

Título: Image Analysis and Recognition

Páginas: 413-422

5th International Conference on Image Analysis and Recognition (ICIAR 2008)

Póvoa de Varzim, Portugal, June 25-27, 2008

Indexação

ISI Proceedings

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-004-3HY

DOI: 10.1007/978-3-540-69812-8_41

Abstract (EN): This paper presents a new classification approach to deal with class imbalance in TLC patterns, which is due to the huge difference between the number of normal and pathological cases as a consequence of the rarity of LSD diseases. The proposed architecture is formed by two decision stages: the first is implemented by a one-class classifier aiming at recognizing most of the normal samples; the second stage is a hierarchical classifier which deals with the remaining outliers that are expected to contain the pathological cases and a small percentage of normal samples. We have also evaluated this architecture by a forest of classifiers, using the majority voting as a, rule to generate the final classification. The results that were obtained proved that this approach is able to overcome some of the difficulties associated with class imbalance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10