Results 21 to 30 of about 3,745,728 (280)
Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Applied Sciences, 2021 Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns.
Michael Heigl +3 more
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Unsupervised Dual Learning for Feature and Instance Selection
IEEE Access, 2020 Feature selection and instance selection are dual operations on a data matrix. Feature selection aims at selecting a subset of relevant and informative features from original feature space, while instance selection at identifying a subset of informative ...
Liang Du, Xin Ren, Peng Zhou, Zhiguo Hu
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Journal of Information Science Theory and Practice, 2021 Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with ...
Qusay Bsoul +3 more
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Unsupervised Feature Selection with Latent Relationship Penalty Term
Axioms, 2023 With the exponential growth of high dimensional unlabeled data, unsupervised feature selection (UFS) has attracted considerable attention due to its excellent performance in machine learning.
Ziping Ma +3 more
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Remote Sensing, 2018 In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available.
Patrick Erik Bradley +2 more
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UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
Applied Sciences, 2023 In most machine learning (ML) applications, data that arrive from heterogeneous views (i.e., multiple heterogeneous sources of data) are more likely to provide complementary information than does a single view.
Fawaz Alarfaj +5 more
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Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection
IEEE Access, 2017 Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR).
Wei Zhou +3 more
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ENTROPY BASED GREEDY UNSUPERVISED FEATURE SELECTION METHOD USING ROUGH SET THEORY FOR CLASSIFICATION
ICTACT Journal on Soft Computing, 2022 Feature selection technique attempts to select and remove irrelevant features while ensuring that an informative subset of features remains in the dataset.
Rubul Kumar Bania, Satyajit Sarmah
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Auto-UFSTool: An Automatic Unsupervised Feature Selection Toolbox for MATLAB [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 Various data analysis research has recently become necessary in to find and select relevant features without class labels using Unsupervised Feature Selection (UFS) approaches. Despite the fact that several open-source toolboxes provide feature selection
Farhad Abedinzadeh Torghabeh +2 more
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Feature selection for modular GA-based classification [PDF]
, 2004 Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification.
Guan, SU, Zhu, F, Zhu, F., Guan, S.
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