Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality 2018
DOI: 10.1145/3293663.3293682
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Further Experiments on A Combination of Linear SVM Weight and ReliefF for Dimensionality Reduction

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Cited by 2 publications
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“…There are two main perspectives of dimensionality reduction: 1) feature selection (irrelevant or unnecessary data features will be reduced, and only key data features will be selected) and 2) feature extraction (the number of data items representing a dataset will be reduced or discarded and new data features will be created). Several research works have been on optimizations and applications of feature selection and feature extraction (Buathong & Jarupunphol, 2018;Manek et al, 2017;Miao & Niu, 2016;Tumthong et al, 2021).…”
Section: Data Mining Applicationsmentioning
confidence: 99%
“…There are two main perspectives of dimensionality reduction: 1) feature selection (irrelevant or unnecessary data features will be reduced, and only key data features will be selected) and 2) feature extraction (the number of data items representing a dataset will be reduced or discarded and new data features will be created). Several research works have been on optimizations and applications of feature selection and feature extraction (Buathong & Jarupunphol, 2018;Manek et al, 2017;Miao & Niu, 2016;Tumthong et al, 2021).…”
Section: Data Mining Applicationsmentioning
confidence: 99%