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首页> 外文会议>2019 7th International Symposium on Digital Forensics and Security >Predicting Daily Activities Effectiveness Using Base-level and Meta level Classifiers
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Predicting Daily Activities Effectiveness Using Base-level and Meta level Classifiers

机译：使用基本级别和元级别的分类器预测日常活动的有效性

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Collecting and analyzing Activities of Daily Living (ADL) could supplement elder care and long-term care services with very sensitive information about elder people and what they do during the day and what challenges they face. Providing care for elder people based on their ADL could let them live actively, independently and healthy. In this paper, we studied the effectiveness of base learners against ensemble methods for predicting ADL. The selected base learners are Naïve Bayes, Bayesian Network, Sequential Minimal Optimization, Decision Table and J48 while the selected ensemble learners are boosting, bagging, decorate and random forest. The dataset was gathered from a wearable accelerometer attached on the chest. The data used in this study is collected from fifteen participants conducting seven activities namely standing up, working at the computer, going up downstairs, standing, walking, walking and talking with someone and talking while standing, walking and going up downstairs. For base learners, J48 achieved the best results in terms of F-measure, precision and recall. Results also showed that Boosting using decision table as the base classifier achieved the best improvement over base classifier. In addition, Bagging was the only ensemble approach that improved the results using all classifiers as base learners. Moreover, Bagging was able to predict five activities out of seven more efficiently than the other approaches while the rotation forest approach was able to predict the remaining two activities more efficiently than the rest. The results also indicated that all approaches took a reasonable time to build the model except Decorate.
机译：收集和分析日常生活活动（ADL）可以利用非常敏感的信息来补充老年人护理和长期护理服务，这些信息包括老年人及其在白天的工作以及面临的挑战。根据他们的ADL为老年人提供护理可以让他们积极，独立和健康地生活。在本文中，我们研究了基础学习者针对用于预测ADL的整体方法的有效性。所选的基础学习者是朴素贝叶斯，贝叶斯网络，顺序最小优化，决策表和J48，而所选的集成学习者则是对森林进行加强，装袋，装潢和随机布置。该数据集是从安装在胸部的可穿戴式加速度计中收集的。这项研究中使用的数据是从15位参与者中收集的，他们进行了七项活动，即站立，在计算机上工作，下楼，站立，行走，步行和与某人交谈以及站立，步行和上楼交谈。对于基础学习者，J48在F测度，准确性和召回率方面取得了最佳结果。结果还表明，使用决策表作为基础分类器的Boosting取得了优于基础分类器的最佳改进。此外，装袋是使用所有分类器作为基础学习者来改善结果的唯一合奏方法。此外，Bagging能够比其他方法更有效地预测七种活动中的五种，而轮伐林方法能够比其余方法更有效地预测其余两种活动。结果还表明，除“装饰”外，所有方法都花费了合理的时间来构建模型。

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