Results 21 to 30 of about 951,800 (293)
Analysis and Evaluation of Feature Selection and Feature Extraction Methods
International Journal of Computational Intelligence Systems, 2023 Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when ...
Rubén E. Nogales, Marco E. Benalcázar
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A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering [PDF]
arXiv, 2021 Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features.
arxiv
Supervised Feature Selection With a Stratified Feature Weighting Method
IEEE Access, 2018 Feature selection has been a powerful tool to handle high-dimensional data. Most of these methods are biased toward the highest rank features which may be highly correlated with each other.
Renjie Chen +4 more
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A Feature Selection Algorithm Performance Metric for Comparative Analysis
Algorithms, 2021 This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems.
Werner Mostert +2 more
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Online Feature Selection with Group Structure Analysis [PDF]
, 2016 Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture and other visual information ...
arxiv +1 more source
Deep Feature Selection Using a Novel Complementary Feature Mask [PDF]
arXiv, 2022 Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of features, which enables better interpretability than feature extraction. However, most existing feature selection approaches, especially deep-learning-based, often focus on the
arxiv
Neighborhood Ranking-Based Feature Selection
IEEE AccessThis article aims to integrate ${k}$ -NN regression, false-nearest neighborhood (FNN), and trustworthiness and continuity (T&C) neighborhood-based measures into an efficient and robust feature selection method to support the identification of ...
Adam Ipkovich, Janos Abonyi
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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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Optimal Feature Aggregation and Combination for Two-Dimensional Ensemble Feature Selection
Information, 2020 Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics.
Machmud Roby Alhamidi, Wisnu Jatmiko
doaj +1 more source
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection [PDF]
arXiv, 2023 Survey data can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features ...
arxiv