A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis

The high dimension of data makes difficult to train and test many classification methods. This work aims to present a new filter Feature Selection Method, called H-Ratio, which can identify pertinent features from data. This method improves results of two previous works focusing on nominal classifiers based on Formals Concepts Analysis. The evaluation of H-Ratio shows that this method performs nominal classifiers processing. Our method has an error rate of 5% (~7% relative improvement over a supervised classification method).