Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono.
<p>Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono.</p>
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2024
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| _version_ | 1852024743337656320 |
|---|---|
| author | Muhammad Asim Shahid (15285640) |
| author2 | Muhammad Mansoor Alam (15285643) Mazliham Mohd Su’ud (15285646) |
| author2_role | author author |
| author_facet | Muhammad Asim Shahid (15285640) Muhammad Mansoor Alam (15285643) Mazliham Mohd Su’ud (15285646) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Asim Shahid (15285640) Muhammad Mansoor Alam (15285643) Mazliham Mohd Su’ud (15285646) |
| dc.date.none.fl_str_mv | 2024-12-03T18:37:20Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0311089.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Bagging_classifier_s_confusion_matrix_for_accuracy_fault_prediction_based_on_CPU-mem_mono_/27955789 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_8a01fc782f7c7cfeaaa094cd91b331c8 |
| identifier_str_mv | 10.1371/journal.pone.0311089.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27955789 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono.Muhammad Asim Shahid (15285640)Muhammad Mansoor Alam (15285643)Mazliham Mohd Su’ud (15285646)BiotechnologyPlant BiologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsimplified resource allocationrecent years duenaive bayes treemem multi classifierhdd multi classifierprimary data resultsnb tree ).less fault predictionfact based analysishighest accuracy percentagemem mono classifier10 folds crosshighest accuracy rate78 %, 95good algorithm complexitynb treefault predictionhighest accuracymono classifier95 %,accuracy ratefold crossdecision treetime complexityalgorithm complexity9 %,xlink ">taking 1past decademany corporationsmake modificationsleast amountincreased significantlyimproving reliabilityexponential riseensure accessibilitydl4jmlp ),decision trees9 seconds11 seconds<p>Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono.</p>2024-12-03T18:37:20ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0311089.g006https://figshare.com/articles/figure/Bagging_classifier_s_confusion_matrix_for_accuracy_fault_prediction_based_on_CPU-mem_mono_/27955789CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/279557892024-12-03T18:37:20Z |
| spellingShingle | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. Muhammad Asim Shahid (15285640) 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 |
| status_str | publishedVersion |
| title | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| title_full | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| title_fullStr | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| title_full_unstemmed | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| title_short | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| title_sort | Bagging classifier’s confusion matrix for accuracy & fault prediction based on CPU-mem mono. |
| topic | 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 |