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

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Abstract

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).

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Trabelsi, M., Meddouri, N., & Maddouri, M. (2017). A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis. In Procedia Computer Science (Vol. 112, pp. 186–194). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.08.227

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