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

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

Antonio V Sousa (Author) Other; Ana Maria Mendonca (Author) FEUP; Aurelio Campilho (Author) FEUP

Conference proceedings International

Title: Image Analysis and Recognition

Pages: 413-422

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

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

Indexing

ISI Proceedings

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10