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Classification is one of the most important topics in machine learning. However, most of these works focus on the two-class classification (i.e., classification into 'positive' and 'negative'), whereas studies on multi-class classification are far from enough. In this study, we develop a novel methodology of multiple classifier systems (MCS) with one-vs-one (OVO) scheme for the multi-class classification task. First, the multi-class classification problem is divided into as many pairs of easier-to-solve binary sub-problems as possible. Subsequently, an optimal MCS is generated for each sub-problem using a roulette-based feature subspace selection and validation procedure. Finally, to identify the final class of a query sample, an OVO aggregation strategy is employed to obtain the class from the confidence score matrix derived from the MCS. To verify the effectiveness and robustness of the proposed approach, a thorough experimental study is performed. The extracted findings supported by the proper statistical analysis indicate the strength of the proposed method with respect to the state-of-the-art methods for multi-class classification problems. [ABSTRACT FROM AUTHOR]