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In this paper we propose a Sequential Ensemble Classification (SEC) technique which is designed to tackle the problem of learning from a data set with an extremely unbalanced distribution of instances among the classes. This system employs a specific decomposition technique that reduces the degree of unbalance in the data by transforming multi-class problem into a sequence of binary class problems. We investigate two different implementations of the proposed method, one based on an ensemble of homogeneous classifiers and a second based on a heterogeneous ensemble of classifiers. A real-world medical data set has been chosen as a case study for the investigation of the proposed method. The data is highly unbalanced, consists of a wide range of class values, some of which contain only a few instances, and which is voluminous. Our experimental results show that both schemes of the SEC system are able to outperform standalone classifiers, with the highest performance being achieved by the homogeneous design of the system.