Visual representation of information remains a key part of exploratory data analysis. This is due to the high number of features in datasets and their increasing complexity, together with users' ability to visually understand information. One of the most common operations in exploratory data analysis is the selection of relevant features in the available data. In multidimensional scenarios, this task is often done with the help of automatic dimensionality reduction algorithms from the machine learning field. In this paper we develop a visual interface where users are integrated into the feature selection process of several machine learning algorithms. Users can work interactively with the algorithms in order to explore the data, compare the results and make the appropriate decisions about the feature selection process.