| Title | Minimizing the imbalance problem in chromatographic profile classification with one-class classifiers |
| Publication Type | Book |
| Year of Publication | 2008 |
| Authors | Sousa, AV, Mendonça, AM, Campilho, A |
| Series Title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci. |
| Volume | 5112 LNCS |
| Number of Pages | 413 - 422 |
| City | Povoa de Varzim |
| ISBN Number | 03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN) |
| Keywords | Class imbalances, Classification (of information), Classification approach, Classifiers, Heidelberg (CO), Hierarchical classifiers, Image analysis, Imaging techniques, Imbalance problems, International conferences, Learning systems, Majority voting, Pathological cases |
| Abstract | 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. © 2008 Springer-Verlag Berlin Heidelberg. |
| URL | http://www.scopus.com/inward/record.url?eid=2-s2.0-47849088119&partnerID=40&md5=df911723c51c6f540287fdd675267725 |
