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A famous field in which it is very possible for each typical dataset to be imbalanced and hard is physician recognition. In such systems there are many customers where a few of them are patient and the others are healthy. So it is very common and possible for a dataset to emerge an imbalanced one. In such a system it is desired to distinguish a patient from a mixture of customers. In a breast cancer detection that is a special case of the mentioned systems, it is desired to discriminate the patient clients from healthy ones. This paper presents an algorithm which is well-suited for and applicable to the field of severe imbalanced datasets. It is efficient in terms of both of the speed and the efficacy of learning. The experimental results show that the performance of the proposed algorithm outperforms some of the best methods in the literature.