首页|University Indonesia Researcher Describes Research in Machine Learning (Assessme nt of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia)
University Indonesia Researcher Describes Research in Machine Learning (Assessme nt of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from the Universi ty Indonesia by NewsRx correspondents, research stated, “Landslide susceptibilit y projections that rely on independent models produce biased results.” The news correspondents obtained a quote from the research from University Indon esia: “This situation will worsen class balance if working with a small populati on. This study proposes a landslide susceptibility prediction model based on res ampling, cross-validation, bootstrap, and random subsampling approaches, which i s integrated with the machine learning model, generalized linear model, support vector machine, random forest, boosted regression trees, classification and regr ession tree, multivariate adaptive regression splines, mixture discriminate anal ysis, flexible discriminant analysis, maximum entropy, and maximum likelihood. T his methodology was applied in Kendari City, an urban area which faced destructi ve erosion. Area under the ROC curve (AUC), true skill statistics (TSS), correla tion coefficient (COR), normalized mutual information (NMI), and correct classif ication rate (CCR) were used to evaluate the predictive accuracy of the proposed model. The results show that the resampling algorithm improves the performance of the standalone model. Results also revealed that standalone models had better performance with the CV algorithm compared to the Bt and RS algorithms.”
University IndonesiaCyborgsEmerging TechnologiesMachine Learning
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