Fast Feature Ranking Algorithm

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant at- tributes, allow making models of classification simpler and easy to un- derstand. The algorithm has some interesting characteristics: lower com- putational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of dis- tance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. In order to test the relevance of the new feature selection algorithm, we compare the results induced by several classifiers before and after applying the feature selection algorithms.