Cancer classification is the critical basis for patient-tailored therapy. Conventional histological analysis tends to be unreliable because different tumors may have similar appearance. The advances in microarray technology make individualized therapy possible. Various machine learning methods can be employed to classify cancer tissue samples based on microarray data. However, few methods can be elegantly adopted for generating accurate and reliable as well as biologically interpretable rules. In this paper, we introduce an approach for classifying cancers based on the principle of minimal rough fringe. For training rough hypercuboid classifiers from gene expression data sets, the method dynamically evaluates all available genes and sifts the genes with the smallest implicit regions as the dimensions of implicit hypercuboids. An unseen object is predicted to be a certain class if it falls within the corresponding class hypercuboid. Based upon the method, ensemble rough hypercuboid classifiers are subsequently constructed. Experimental results on some open cancer gene expression data sets show that the proposed method is capable of generating accurate and interpretable rules compared with some other machine learning methods. Hence, it is a feasible way of classifying cancer tissues in biomedical applications.