Classifier errors of AdaBoostM1 based on primary data in accuracy & fault prediction.

<p>Classifier errors of AdaBoostM1 based on primary data in accuracy & fault prediction.</p>

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Bibliographic Details
Main Author:	Muhammad Asim Shahid (15285640) (author)
Other Authors:	Muhammad Mansoor Alam (15285643) (author), Mazliham Mohd Su’ud (15285646) (author)
Published:	2024
Subjects:	Biotechnology Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified simplified resource allocation recent years due naive bayes tree mem multi classifier hdd multi classifier primary data results nb tree ). less fault prediction fact based analysis highest accuracy percentage mem mono classifier 10 folds cross highest accuracy rate 78 %, 95 good algorithm complexity nb tree fault prediction highest accuracy mono classifier 95 %, accuracy rate fold cross decision tree time complexity algorithm complexity 9 %, xlink "> taking 1 past decade many corporations make modifications least amount increased significantly improving reliability exponential rise ensure accessibility dl4jmlp ), decision trees 9 seconds 11 seconds
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