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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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Agnostic Feature Selection [PDF]
, 2020 Unsupervised feature selection is mostly assessed along a supervised learning setting, depending on whether the selected features efficiently permit to predict the (unknown) target variable. Another setting is proposed in this paper: the selected features aim to efficiently recover the whole dataset.
Doquet, Guillaume Florent +1 more
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Online Feature Selection with Streaming Features [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed.
Xindong Wu 0001 +4 more
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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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ACM Computing Surveys, 2017 Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean,
Jundong Li +6 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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Ontology-Based Feature Selection: A Survey
Future Internet, 2021 The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information.
Konstantinos Sikelis +2 more
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Redundancy Is Not Necessarily Detrimental in Classification Problems
Mathematics, 2021 In feature selection, redundancy is one of the major concerns since the removal of redundancy in data is connected with dimensionality reduction. Despite the evidence of such a connection, few works present theoretical studies regarding redundancy.
Sebastián Alberto Grillo +9 more
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Feature Selection Embedded Robust K-Means
IEEE Access, 2020 Clustering is one of the most important unsupervised learning problems in machine learning. As one of the most widely used clustering algorithms, K-means has been studied extensively.
Qian Zhang, Chong Peng
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Nested ensemble selection: An effective hybrid feature selection method
Heliyon, 2023 It has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features.
Firuz Kamalov +4 more
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