Effectively reducing the size of the training set for classifier training is a rapidly advancing area of machine learning research. In this context, meta instance selection has emerged as a promising approach. This method transforms the instance selection problem into a classification task and employs a meta-classifier to determine which samples should be kept and which should be removed. In this study, we focus on identifying the optimal meta-classifier by evaluating several popular predictive models, including balanced random forests, standard random forests, gradient-boosted trees, k-nearest neighbors, and multi-layer perceptron networks. The results demonstrate that the balanced random forest outperforms all other models, yielding the best performance by a significant margin.