Discussing the article: "A feature selection algorithm using energy based learning in pure MQL5"
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Check out the new article: A feature selection algorithm using energy based learning in pure MQL5.
In this article we present the implementation of a feature selection algorithm described in an academic paper titled,"FREL: A stable feature selection algorithm", called Feature weighting as regularized energy based learning.
In the realm of algorithmic trading, the widespread use of machine learning has prompted the adoption of data mining techniques to uncover hidden patterns in financial data. Within this landscape, practitioners often grapple with the challenge of sorting through numerous variables to identify those most likely to be beneficial in achieving specific goals or solving particular problems. In this article, we explore the implementation of a feature selection algorithm aimed at assessing the relevance of a set of candidate variables for a given prediction task.
Yun Li, Jennie Si, Guojing Zhou, Shasha Huang, and Songcan Chen co-authored a research paper titled "FREL: A Stable Feature Selection Algorithm." This paper introduces an algorithm named Feature Weighting as Regularized Energy-Based Learning (FREL), which serves as a feature selection or weighting technique designed to offer both accuracy and stability. In our discussion, we provide an overview of the theoretical underpinnings behind regularized energy-based learning and feature weighting. Furthermore, we illustrate the efficacy of the proposed approach through the implementation of an example MQL5 program, crafted as a script, to highlight the method's potential as a tool for feature selection.
Author: Francis Dube