Microarrays produce expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification. We performed a direct integration of individual microarrays with same biological objectives by converting an expression value into a rank value within a sample and built a classifier based on rank comparison. Our classifier is an ensemble method, which has k top-scoring decision rules. Each rule contains a number of genes, a relationship between those genes, and a class label. Current classifiers fix the number of genes in each rule as a pair or a triple. In this paper, we generalized the number of genes involved in each rule. Generalizing the number of genes increases the robustness and the reliability of the classifier. Our algorithm saves resources by combining shorter rules to build a longer-rule, shows a rapid convergence toward its high-scoring rule list, and outperforms the current methods in run-time and classification accuracy.