Evolutionary computation based nonlinear transformations to low dimensional spaces for sensor data fusion and Visual Data Mining

J. J. Valdés
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引用次数: 3

Abstract

Data fusion approaches are nowadays needed and also a challenge in many areas, like sensor systems monitoring complex processes. This paper explores evolutionary computation approaches to sensor fusion based on unsupervised nonlinear transformations between the original sensor space (possibly highly-dimensional) and lower dimensional spaces. Domain-independent implicit and explicit transformations for Visual Data Mining using Differential Evolution and Genetic Programming aiming at preserving the similarity structure of the observed multivariate data are applied and compared with classical deterministic methods. These approaches are illustrated with a real world complex problem: Failure conditions in Auxiliary Power Units in aircrafts. The results indicate that the evolutionary approaches used were useful and effective at reducing dimensionality while preserving the similarity structure of the original data. Moreover the explicit models obtained with Genetic Programming simultaneously covered both feature selection and generation. The evolutionary techniques used compared very well with their classical counterparts, having additional advantages. The transformed spaces also help in visualizing and understanding the properties of the sensor data.
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基于进化计算的低维空间非线性变换传感器数据融合与可视化数据挖掘
如今,数据融合方法在许多领域都是需要的,也是一个挑战,比如传感器系统监测复杂过程。本文探讨了基于原始传感器空间(可能是高维)和低维空间之间的无监督非线性变换的传感器融合的进化计算方法。将基于差分进化和遗传规划的域无关隐式和显式变换应用于可视化数据挖掘,目的是保持观测到的多变量数据的相似结构,并与经典的确定性方法进行比较。这些方法以一个现实世界的复杂问题为例进行了说明:飞机辅助动力装置的故障情况。结果表明,所采用的进化方法在保持原始数据相似结构的同时,能够有效地降低维数。此外,遗传规划得到的显式模型同时涵盖了特征选择和特征生成。所使用的进化技术与它们的经典对手相比非常好,具有额外的优势。转换后的空间还有助于可视化和理解传感器数据的属性。
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