Feature selection techniques for machine learning: a survey of more than two decades of researchMACHINE learningFEATURE selectionALGORITHMSLearning algorithms can be less effective on datasets with an extensive feature space due to the presence of irrelevant and redundant features....
Filter-based feature selection approaches are based on data intrinsic attributes such as feature correlation or statistics. These approaches assess the value of each characteristic alone or in pairs without taking into account the performance of a particular learning algorithm. Filter-based approaches are...
perovskites; materials design; machine learning; feature selection1. Introduction Machine learning (ML), as an interdisciplinary technique covering multiple fields of statistics, computer science, and mathematics, has been widely used in the medical, bioinformatics, financial, and agriculture fields [1,2...
How to Choose Feature Selection Methods For Machine Learning Numerical Input, Numerical Output This is a regression predictive modeling problem with numerical input variables. The most common techniques are to use a correlation coefficient, such as Pearson’s for a linear correlation, or rank-based ...
Feature selection algorithms in machine learning are techniques for choosing the most relevant and useful features for building predictive models. Many typical applications in machine learning, from customer segmentation to medical diagnosis, arise from complex interactions between all types of variables. ...
In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be ...
Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rou...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Feature engineering is the first step in a machine learning pipeline and involves all the techniques adopted to clean existing datasets, increase their signal-noise ratio, and reduce their dimensionality. Most algorithms have strong assumptions about the input data, and their performances can be ...
2.1.5Machine Learning-Based Approaches Machine learningrepresents another alternative for detecting design defects. Catalet al.[33]used differentmachine learning algorithmsto predict defective modules. They investigated the effect of dataset size, metrics set, and feature selection techniques forsoftware faul...