On Feature Selection: a New Filter Model

We focus on the filter approach of feature selection. We exploit geometrical characterics of the learning set to build an estimation criterion based on a quadratic entropy. The distribution of this criterion is approximately normal, that allows the construction of a non parametrical statistical test to assess the relevance of feature subsets. We use the critical threshold of this test, called the test of Relative Certainty Gain, in a forward selection algorithm. We present some experimental results both on synthetic and natural domains belonging to the UCI database repository, which show significantly improvments on the accuracy estimates.