MicroRNA (miRNA) plays vital roles in biological processes like RNA splicing and regulationof gene expression. Studies have revealed that there might be possible links between oncogenesisand expression profiles of some miRNAs, due to their differential expression betweennormal and tumor tissues. However, the automatic classification of miRNAs into different categoriesby considering the similarity of their expression values has rarely been addressed. This articleproposes a solution framework for solving some real-life classification problems related to cancer,miRNA, and mRNA expression datasets. In the first stage, a multiobjective optimization basedframework, non-dominated sorting genetic algorithm II, is proposed to automatically determinethe appropriate classifier type, along with its suitable parameter and feature combinations, pertinentfor classifying a given dataset. In the second page, a stack-based ensemble technique is employed toget a single combinatorial solution from the set of solutions obtained in the first stage. The performanceof the proposed two-stage approach is evaluated on several cancer and RNA expression profiledatasets. Compared to several state-of-the-art approaches for classifying different datasets, ourmethod shows supremacy in the accuracy of classification.