2019
DOI: 10.37272/jiecr.2019.10.19.5.185
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Exploring Cryptocurrency Influence factors Using Feature Selection Algorithm

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“…Feature selection can find the subset of the most useful features among raw data for improving classification or model accuracy and is used to quantify the influence among features [53]. Moreover, it is used to remove redundant or irrelevant features in machine learning so as to create predictive models and find a combination of features related to the class to be predicted [47,54].…”
Section: Feature Selectionmentioning
confidence: 99%
“…Feature selection can find the subset of the most useful features among raw data for improving classification or model accuracy and is used to quantify the influence among features [53]. Moreover, it is used to remove redundant or irrelevant features in machine learning so as to create predictive models and find a combination of features related to the class to be predicted [47,54].…”
Section: Feature Selectionmentioning
confidence: 99%