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Feature Selection with the Boruta Package
Journal of Statistical Software, 2010 This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm.
Miron B. Kursa, Witold R. Rudnicki
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(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network [PDF]
Computer Vision and Pattern Recognition, 2021 Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one of the essential components of road scene perception that
Ran Cheng +4 more
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Survey of feature selection and extraction techniques for stock market prediction
Financial Innovation, 2023 In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions.
Htet Htet Htun, Michael Biehl, N. Petkov
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Shapley values for feature selection: The good, the bad, and the axioms [PDF]
IEEE Access, 2021 The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a large extent, to a solid theoretical foundation, including four “favourable and fair” axioms for attribution in transferable utility games.
D. Fryer, Inga Strümke, Hien D. Nguyen
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Hybrid-Recursive Feature Elimination for Efficient Feature Selection
Applied Sciences, 2020 As datasets continue to increase in size, it is important to select the optimal feature subset from the original dataset to obtain the best performance in machine learning tasks.
Hyelynn Jeon, Sejong Oh
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A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction
Frontiers in Bioinformatics, 2022 Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However,
N. Pudjihartono +3 more
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A comprehensive survey on recent metaheuristics for feature selection
Neurocomputing, 2022 Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades,
Tansel Dökeroğlu +2 more
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Adapting Feature Selection Algorithms for the Classification of Chinese Texts
Syst., 2023 Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on text features, so the accuracy of feature selection is ...
Xuan Liu +7 more
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Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)
IEEE Access, 2021 Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem.
Prachi Agrawal +3 more
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Biogeography-based optimization for feature selection
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Data clustering has many applications in medical sciences, banking, and data mining. K-means is the most popular data clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has some limitations, which may affect its
Mandana Gholami +2 more
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