Results 11 to 20 of about 7,765,049 (335)
Feature selection in machine learning: A new perspective
Neurocomputing, 2018 High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce ...
Jie Cai +3 more
semanticscholar +3 more sources
Wrappers for Feature Subset Selection
Artificial Intelligence, 1997 Ron Kohavi, G. John
semanticscholar +3 more sources
Radiomics and Its Feature Selection: A Review
Symmetry, 2023 Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying ...
Wenchao Zhang, Yu Guo, Qiyu Jin
semanticscholar +1 more source
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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Digging into acceptor splice site prediction : an iterative feature selection approach [PDF]
, 2004 Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data.
A.I. Blum +18 more
core +2 more sources
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
doaj +1 more source
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [PDF]
Computer Vision and Pattern RecognitionSelf-supervised feature reconstruction methods have shown promising advances in industrial image anomaly de-tection and localization. Despite this progress, these meth-ods still face challenges in synthesizing realistic and di-verse anomaly samples, as ...
Ximiao Zhang, Min Xu, Xiuzhuang Zhou
semanticscholar +1 more source
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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