Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Dissimilarity-based classification of chromatographic profiles
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper presents a method for automating the selection of the rejection rate of one-class classifiers aiming at optimizing the classifier performance. These classifiers are used in a new classification approach to deal with class imbalance in Thin-Layer Chromatography (TLC) patterns, which is due to the huge difference between the number of normal and pathological cases, as a consequence of the rarity of Lysosomal Storage Disorders (LSD) diseases. The classification is performed in two decision stages, both implemented using optimized one-class classifiers: the first stage aims at recognizing most of the normal samples; the outliers of this first decision level are presented to the second stage, which is a multiclassifier prepared to deal with both pathological and normal patterns. The results that were obtained proved that the proposed methodology is able to overcome some of the difficulties associated with the choice of the rejection rate of one-class classifiers, and generally contribute to the minimization of the imbalance problem in TLC pattern classification.