Data analysis via supervised learning tasks is among the most common data mining techniques. The objective of meta-learning is to generate a user-supporting system for selection of the most appropriate supervised learning algorithms for such tasks. The meta-learning framework is usually based upon a classification on the meta-level often disregarding a large amount of information gained during the induction process. The performance of supervised learning algorithms is also clearly dependent on the quality of the data. And, considering only a small subset of meta-attributes may significantly reduce both the time and effort applied for the corresponding measurement process. In this book, the extent to which the issues above impact the performance of a meta-learning system is evaluated and solutions for remedying the difficulties observed are presented. In particular, the accuracies of the base learners are predicted, thus avoiding the rigid decision on a single-best learner. Subsequently, the severity of data quality issues is investigated. In order to improve the performance of the meta-learning system, various feature selection approaches are employed. Experimental evaluations performed on real-world domains show that the ideas developed in this book are indeed useful in alleviating some of the difficulties encountered in the area of meta-learning.