Discussing the article: "Mutual information as criteria for Stepwise Feature Selection"
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Check out the new article: Mutual information as criteria for Stepwise Feature Selection.
In this article, we present an MQL5 implementation of Stepwise Feature Selection based on the mutual information between an optimal predictor set and a target variable.
Mutual information is a valuable tool for identifying effective predictors, especially when dealing with complex, nonlinear relationships. It can uncover dependencies that other methods might miss, making it particularly suitable for models that can exploit such intricacies. This article explores the application of mutual information in feature selection, focusing on the algorithm proposed by Hanchuan Peng, Fuhui Long, and Chris Ding in their research paper titled, "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy".
We will begin by discussing the estimation of mutual information for continuous variables, then delve into the feature selection process itself. Finally, we will illustrate the algorithm's effectiveness through examples involving both synthetic and real-world datasets.
Author: Francis Dube