A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis
Marwa Trabelsi, Nida Meddouri, Mondher Maddouri · Procedia Computer Science · 2017
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).