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Dual-Regularized Feature Selection for Class-Specific and Global Feature Associations [PDF]
EntropyUnderstanding feature associations is vital for selecting the most informative features. Existing methods primarily focus on global feature associations, which capture overall relationships across all samples.
Chenchen Wang +4 more
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Unsupervised feature selection algorithm based on L 2,p-norm feature reconstruction. [PDF]
PLoS ONETraditional subspace feature selection methods typically rely on a fixed distance to compute residuals between the original and feature reconstruction spaces.
Wei Liu +5 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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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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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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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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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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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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Feature extraction for epileptic seizure detection using machine learning
Current Medicine Research and Practice, 2020 Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming.
Renuka Mohan Khati, Rajesh Ingle
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