Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm
- Publisher:
- Public Library of Science (PLoS)
- Publication Type:
- Journal Article
- Citation:
- PLOS ONE, 2023, 18, (4), pp. e0284209
- Issue Date:
- 2023
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The benefits and opportunities offered by cloud computing are among the fastest growing technologies in the computer industry Additionally it addresses the difficulties and issues that make more users more likely to accept and use the technology The proposed research comprised of machine learning ML algorithms is Na ve Bayes NB Library Support Vector Machine LibSVM Multinomial Logistic Regression MLR Sequential Minimal Optimization SMO K Nearest Neighbor KNN and Random Forest RF to compare the classifier gives better results in accuracy and less fault prediction In this research the secondary data results CPU Mem Mono give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80 20 77 01 70 30 76 05 and 5 folds cross validation 74 88 and CPU Mem Multi in terms of 80 20 89 72 70 30 90 28 and 5 folds cross validation 92 83 Furthermore on HDD Mono the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80 20 87 72 70 30 89 41 and 5 folds cross validation 88 38 and HDD Multi in terms of 80 20 93 64 70 30 90 91 and 5 folds cross validation 88 20 Whereas primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80 20 97 14 70 30 96 19 and 5 folds cross validation 95 85 in the primary data results but the algorithm complexity 0 17 seconds is not good In terms of 80 20 95 71 70 30 95 71 and 5 folds cross validation 95 71 SMO has the second highest accuracy and less fault prediction but the algorithm complexity is good 0 3 seconds The difference in accuracy and less fault prediction between RF and SMO is only 13 and the difference in time complexity is 14 seconds We have decided that we will modify SMO Finally the Modified Sequential Minimal Optimization MSMO Algorithm method has been proposed to get the highest accuracy less fault prediction errors in terms of 80 20 96 42 70 30 96 42 5 fold cross validation 96 50
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