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The presence of noise in real data sets can harm the predictive performance of machine learning algorithms. There are several noise filtering techniques whose goal is to improve the quality of the data in classification tasks. These techniques usually scan the data for noise identification in a preprocessing step. Nonetheless, this is a non-trivial task and some noisy data can remain unidentified, while safe data can also be removed. The bias of each filtering technique influences its performance on a particular data set. Therefore, there is no single technique that can be considered the best for all domains or data distribution and choosing a particular filter is not straightforward. Meta-learning has been largely used in the last years to support the recommendation of the most suitable machine learning algorithm(s) for a new data set. This paper presents a meta-learning recommendation system able to predict the expected performance of noise filters in noisy data identification tasks. For such, a meta-base is created, containing meta-features extracted from several corrupted data sets along with the performance of some noise filters when applied to these data sets. Next, regression models are induced from this meta-base to predict the expected performance of the investigated filters in the identification of noisy data. The experimental results show that meta-learning can provide a good recommendation of the most promising filters to be applied to new classification data sets. [ABSTRACT FROM AUTHOR]