This study presents two novel approaches for developing a multiple-meta-instance selection method, an advanced algorithm designed for efficient pruning of training sample in classification problems. The proposed meta-instance selection framework reformulates the traditional instance selection problem by introducing a meta-feature space, a problem-agnostic representation space. The transformation enables instance selection to be framed as a classification task in the meta-feature space, facilitating efficient computation with a time complexity of O(nlog(n)). A standard classification algorithm, such as Random Forest, can then be employed in the meta-feature space to determine the inclusion or exclusion of individual samples. To enhance performance, we explore two strategies for combining multiple meta-instance selection algorithms: (1) constructing an ensemble of meta-classifiers and (2) concatenating many meta-sets. Experimental evaluations demonstrate that the meta-set concatenation approach surpasses both classical instance selection techniques and existing meta-instance selection methods. Moreover, the proposed algorithm significantly accelerates the instance selection process—achieving even by two or three orders of magnitude speed-up, depending on dataset size and the reference instance selection method.