On the behavior of feature selection methods dealing with noise and relevance over synthetic scenarios

Adequate identification of relevant features is fundamental in real world scenarios. The problem is specially important when the datasets have a much larger number of features than samples. However, in most cases, the relevant features in real datasets are unknown. In this paper several synthetic datasets are employed to test the effectiveness of different feature selection methods over different artificial classification scenarios, such as altered features (noise), presence of a crescent number of irrelevant features and a small ratio between number of samples and number of features. Six filters and two embedded methods are tested over five synthetic datasets, so as to be able to choose a robust and noise tolerant method, paving the way for its application to real datasets in the classification domain.