Interpretation of nonlinear relationships between process variables by use of random forests
Interpretation of nonlinear relationships between process variables by use of random forests
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DOI：
10.1016/j.mineng.2012.05.008
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发表时间：
2012-08-01
影响因子：
4.8
通讯作者：
Aldrich, Chris
中科院分区：
文献类型：
--
作者：
Auret, Lidia;Aldrich, Chris
更好地理解过程现象取决于对捕捉过程变量之间关系的模型的解释。虽然线性回归在矿物加工工业中经常用于此目的，但在变量之间的关系是非线性或复杂的情况下，它可能没有用。在这些情况下，非线性方法，如神经网络或决策树，可以用来开发可靠的模型，而不必给出任何特定的或明确的洞察过程和目标变量之间的关系。这是一个主要的缺点，在这种情况下，这些信息将是非常重要的，如在故障识别或获得更好地了解的基本面的一个process.In本文中，使用变量的重要性措施和部分依赖图产生的随机森林模型提出了一个实用的工具，可以用来克服这个问题。特别是，它示出了重要的变量可以被标记的适当的阈值所产生的包括在系统中的虚拟变量。此外，研究结果表明，随机森林模型可以可靠地识别个体变量的影响，即使在存在高水平的加性噪声的情况下。这将使其成为持续过程改进和异常过程行为根本原因分析的有用工具。(C)2012爱思唯尔有限公司保留所有权利。
Better understanding of process phenomena is dependent on the interpretation of models capturing the relationships between the process variables. Although linear regression is used routinely in the mineral process industries for this purpose, it may not be useful where the relationships between variables are nonlinear or complex. Under these circumstances, nonlinear methods, such as neural networks or decision trees can be used to develop reliable models, without necessarily giving any particular or explicit insight into the relationships between the process and the target variables. This is a major drawback in situations where such information would be very important, such as in fault identification or gaining a better understanding of the fundamentals of a process.In this paper, the use of variable importance measures and partial dependency plots generated by random forest models are proposed as a practical tool that can be used to surmount this problem. In particular, it is shown that important variables can be flagged by appropriate threshold generated by inclusion of dummy variables in the system. Moreover, the results of the study indicate that random forest models can reliably identify the influence of individual variables, even in the presence of high levels of additive noise. This would make it a useful tool in continuous process improvement and root cause analysis of abnormal process behaviour. (C) 2012 Elsevier Ltd. All rights reserved.