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首页> 外文期刊>Computing >Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity
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Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity

机译：基于EVoludation-EAC实例 - 基于EAC实例 - 以极端连接辅助生活中的快速数据流挖掘算法

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In modern healthcare, sensing technologies such as IoT empower the quality of assisted living service by knowing what a resident is doing in real-time. Using extreme connectivity and cloud computing in a smart home, where a collection of sensors is installed, the sensors sample continuously from the movements of the resident as well as ambient data from the surrounding inside the house. Automatic human activity recognition of the resident's activities is one of the key components of assisted living in smart home. For monitoring in-home safety, the ability in recognizing abnormal activities such as accident, falling, acute disease attack (e.g. asthma, stroke, etc.), fainting, wobbling, is particularly important. The detection and machine learning process must be both accurate and fast, to cope with the real-time activity recognition. To this end, a novel streamlined sensor data processing method is proposed called Evolutionary Expand-and-Contract Instance-based Learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, then the subspaces which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates scholastically instead of deterministically by evolutionary optimization which approximates the best subgroup. Followed by data stream mining, the machine learning for activity recognition is done on the fly. This approach is unique and suitable for such extreme connectivity scenario where precise feature selection is not required, and the relative importance of each feature among the sensor data changes over time. This stochastic approximation method is fast and accurate, offering an alternative to traditional machine learning method for smart home activity recognition application. Our experimental results show computing advantages over other classical approaches.
机译：在现代医疗保健中，通过了解居民实时正在做的事情，传感技术，例如IOT授权辅助生活服务的质量。在智能家居中使用极端连接和云计算，其中安装了传感器的集合，传感器从居民的运动中连续进行样本以及从房屋内部的周围的环境数据。居民活动的自动人类活动认可是智能家居辅助的关键组成部分之一。为了监测家庭安全，能够识别出意外，坠落，急性疾病攻击（例如哮喘，中风等），晕倒，摆动等异常活动的能力尤为重要。检测和机器学习过程必须准确且快速，以应对实时活动识别。为此，提出了一种新颖的简化传感器数据处理方法，称为进化扩展和合同实例的学习算法（EAC-IBL）。多变量数据流首先扩展到许多子空间中，然后选择对应于特征特性的子空间并冷凝成显着的特征子集。选择通过近似最佳子组的进化优化来校友而不是确定性地操作。其次是数据流挖掘，机器学习活动识别是在飞行中完成的。这种方法是唯一的，适用于不需要精确特征选择的这种极端连接场景，以及传感器数据之间的每个特征的相对重要性随时间而变化。这种随机近似方法快速准确，为智能家庭活动识别应用提供传统的机器学习方法提供替代方案。我们的实验结果表明了与其他经典方法相比的计算优势。

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