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Titlebook: Web Information Systems and Applications; 16th International C Weiwei Ni,Xin Wang,Yukun Li Conference proceedings 2019 Springer Nature Swit

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Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsuracy of defect prediction, dozens of supervised and unsupervised methods have been put forward and achieved good results in this field. One limiting factor of defect prediction is that the data size of defect data is not big, which restricts the scope of application with defect prediction models. I
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Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsuracy of defect prediction, dozens of supervised and unsupervised methods have been put forward and achieved good results in this field. One limiting factor of defect prediction is that the data size of defect data is not big, which restricts the scope of application with defect prediction models. I
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Using Behavior Data to Predict the Internet Addiction of College Studentsiscovering students’ internet addiction tendencies and making correct guidance for them timely are necessary. However, at present, the research methods used on analyzing students’ internet addiction are mainly questionnaire and statistical analysis which relays on the domain experts heavily. Fortuna
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Conference proceedings 2019 China, in September 2019.  The 39 revised full papers and 33 short papers presented were carefully reviewed and selected from 154 submissions. The papers are grouped in topical sections on. .machine learning and data mining, cloud computing and big data, information retrieval, natural language proc
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Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsll bring better defect prediction performance with supervised and unsupervised models or not. The results of our experiment reveal that larger-scale dataset doesn’t bring improvements of both supervised and unsupervised classifiers.
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