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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 naÂ¿ve 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.