Boundary localization in an image pyramid
Pattern Recognition
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing
Machine Learning - Special issue on learning with probabilistic representations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
The class imbalance problem: A systematic study
Intelligent Data Analysis
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bayesian fluorescence in situ hybridisation signal classification
Artificial Intelligence in Medicine
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Solving a multiclass classification task using a small imbalanced database of patterns of high dimension is difficult due to the curse-of-dimensionality and the bias of the training toward the majority classes. Such a problem has arisen while diagnosing genetic abnormalities by classifying a small database of fluorescence in situ hybridization signals of types having different frequencies of occurrence. We propose and experimentally study using the cytogenetic domain two solutions to the problem. The first is hierarchical decomposition of the classification task, where each hierarchy level is designed to tackle a simpler problem which is represented by classes that are approximately balanced. The second solution is balancing the data by up-sampling the minority classes accompanied by dimensionality reduction. Implemented by the naive Bayesian classifier or the multilayer perceptron neural network, both solutions have diminished the problem and contributed to accuracy improvement. In addition, the experiments suggest that coping with the smallness of the data is more beneficial than dealing with its imbalance.