Results 11 to 20 of about 2,759,833 (308)
Threshold Adaptation for Improved Wrapper-Based Evolutionary Feature Selection. [PDF]
Biomimetics (Basel)Feature selection is essential for enhancing classification accuracy, reducing overfitting, and improving interpretability in high-dimensional datasets.
Mlakar U, Fister I, Fister I.
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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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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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A Multi-Scale Feature Selection Method for Steganalytic Feature GFR
IEEE Access, 2020 The Rich Model of the Gabor filter (referred to as the GFR steganalytic feature) can detect JPEG-adaptive steganography objects. However, feature dimensionality that is too high will lead to too much computation and will correspondingly reduce the ...
Xinquan Yu +4 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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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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Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index [PDF]
Computer Science Journal of Moldova, 2022 Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset.
Usharani Bhimavarapu, M. Sreedevi
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Optimization for Gene Selection and Cancer Classification
Proceedings, 2021 Recently, gene selection has played an important role in cancer diagnosis and classification. In this study, it was studied to select high descriptive genes for use in cancer diagnosis in order to develop a classification analysis for cancer diagnosis ...
Hülya Başeğmez +2 more
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Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection
Entropy, 2016 Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings.
Jaesung Lee, Dae-Won Kim
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