Feature selection is called wrapper whenever the classification algorithm is used in the selection procedure. Our approach makes use of linear classifiers wrapped into a genetic algorithm. As a proof of concept we check its performance against the UCI spam filtering problem showing that the wrapping of linear neural networks is the best. However, making sense of data involves not only selecting input features but also output features. Generally, this is considered too much of a human task to be addressed by computers. Only a few algorithms, such as association rules, allow the output to change. One of the advantages of our approach is that it can be easily generalized to search for outputs and relevant inputs at the same time. This is addressed at the end of the paper and it is currently being investigated.