摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx记者来自印度尼西亚大学的新闻报道，研究表明，"依靠独立模型的滑坡敏感性和预测会产生有偏差的结果"。新闻记者引用了印度大学的一句话:“如果在小人口中工作,这种情况将恶化阶级平衡,本研究提出了一种基于重复抽样、交叉验证、自举和随机子抽样方法的滑坡易感性预测模型,它将机器学习模型、广义线性模型、支持向量机模型、随机森林模型、增强回归树、分类回归树和回归树相结合。”应用多元自适应回归样条曲线、混合判别分析、柔性判别分析、最大熵和最大似然等方法,对面临破坏侵蚀的肯达里市进行了ROC曲线下面积(AUC),真技能统计(TSS),相关系数(COR),归一化互信息(NMI),结果表明，重采样算法提高了独立模型的性能，与Bt和RS算法相比，CV算法具有更好的性能。
Abstract
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.”
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