Using a novel merit for feature selection based on rough set theory
Mohammad Mohtashami, Mahdi Eftekhari · 2018
Feature selection in microarray datasets has become one of the most interesting subjects in machine learning and data mining. Binary microarray datasets often consist of thousands of features with a small number of samples and the distribution of classes in them is imbalanced. This paper proposes a new merit based on rough set theory that is inspired by correlation-based merit and named rough set-based merit. Rough set-based merit is applied in rough set quick reduct algorithm to select a significant subset of features. The experimental results in section 4, show the robustness and benefits of the proposed method versus other existing methods in the literature.