Tagging fireworkers activities from body sensors under distribution drift

机译：在分布漂移下从人体传感器标记消防员的活动

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We describe our submission to the AAIA'15 Data Mining Competition, where the objective is to tag the activity of firefighters based on vital functions and movement sensor readings. Our solution exploits a selective naive Bayes classifier, with optimal preprocessing, variable selection and model averaging, together with an automatic variable construction method that builds many variables from time series records. The most challenging part of the challenge is that the input variables are not independent and identically distributed (i.i.d.) between the train and test datasets. We suggest a methodology to alleviate this problem, that enabled to get a final score of 0.76 (team marcb).

机译：我们描述了我们向AIAIA'15数据挖掘竞赛提交的材料，该竞赛的目的是根据重要功能和运动传感器读数来标记消防员的活动。我们的解决方案利用选择性朴素的贝叶斯分类器，具有最佳的预处理，变量选择和模型平均，以及自动变量构建方法，该方法可从时间序列记录中构建许多变量。挑战中最具挑战性的部分是输入变量不是独立的，并且在训练数据集和测试数据集之间没有相同的分布（即i.d.）。我们建议一种缓解此问题的方法，使最终得分为0.76（team marcb）。

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《Federated Conference on Computer Science and Information Systems》|2015年|389-396|共8页
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Boulle Marc;
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Bayes methods; data mining; pattern classification; AAIA'lS data mining competition; body sensor; movement sensor reading; naive Bayes classifier; optimal preprocessing; time series records; Accelerometers; Accuracy; Data preprocessing; Input variables; Yttrium;

机译：贝叶斯方法;数据挖掘;模式分类; AAIA'S数据挖掘竞赛;人体传感器;运动传感器阅读;朴素贝叶斯分类器;最佳预处理;时间序列记录;加速度计;精度;数据预处理;输入变量;钇;

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Tagging fireworkers activities from body sensors under distribution drift

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