Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.

中文翻译：

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评估两阶段多元数据清理方法对提高机器学习分类器性能的影响：人类活动识别的案例研究。

人类活动识别（HAR）是一个流行的研究领域。该领域的项目成果可能会影响痴呆症等疾病患者的生活质量。HAR主要致力于将机器学习分类器应用于来自加速度计等低级传感器的数据。这些分类器的性能可以通过适当的培训过程来提高。为了改善训练过程，使用了多元离群值检测来改善训练集中数据的质量，进而提高分类器的性能。使用KNN和随机森林（RF）分类器评估了该技术的影响。对于KNN，分类器的性能从55.9％提高到63.59％。