SGER: Feature Selection with Ensembles for Complex Systems

本研究提出在复杂系统的高维、混合（数值和分类预测和/或响应）、脏的、非传统数据的具有挑战性的领域中开发特征（变量）选择方法。复杂的系统产生丰富、广泛的数据集，其中包含数十到数百（甚至数千）个变量，跨学科研究团队经常面临从这些信息中学习的挑战。提出了一种混合集成策略，将决策树的串行集成与并行集成相结合，用于特征选择。不需要构造潜在变量。相反，将开发考虑掩蔽效应和冗余的可变重要性分数，并通过人工生成的变量提供统计上有效的结论。这些方法将面临固有的数据挑战以及非线性模型、相互作用、不同震级和尺度的影响。将利用广泛传播的计算能力，对这一问题采取现代、全面的办法。如果成功，这项研究的结果将为复杂系统的建模提供一个变革性的解决方案（利用广泛分布的计算资源）。来自这些系统的数百种不同测量在概念上对系统的理解和技术上对本研究将解决的模型性能造成了瓶颈。一个跨学科的研究团队将能够应用开发的方法来识别关键特征、冗余特征、紧凑模型等，并具有统计有效性。美国国家科学基金会的重点领域：地球系统、自然和人类系统耦合动力学、材料使用和传染病生态学提供了例子，复杂制造、供应链、设计优化和运输提供了额外的例子。而不是有限的数据假设，这些方法将被开发应用于高维，脏的，冗余的，缺失的，混合的数据和非线性，这些系统通常需要的交互模型的背景下。

This research proposes to develop methods for feature (variable) selection in the challenging domain of high-dimensional, mixed (numerical and categorical predictors and/or responses), dirty, nontraditional data from complex systems. Complex systems generate rich, wide data sets with dozens to hundreds (to even thousands) of variables and interdisciplinary research teams are often challenged to learn from such information. A hybrid ensemble strategy is proposed for feature selection that combines both serial and parallel ensembles of decision trees. Latent variables need not be constructed. Instead, variable importance scores will be developed that consider masking effects and redundancy, and provide statistically valid conclusions through artificial, generated variables. The methods will confront the inherent data challenges as well as nonlinear models, interactions, effects of different magnitudes and scales. Leverage of the computational capabilities that are widely disseminated will be made for a modern, comprehensive approach to this problem.If successful, the results of this research will provide a transformative solution (that leverages widely-disseminated computing resources) for modeling complex systems. Hundreds of different measurements from these systems create a bottleneck both conceptually for an understanding of the system and technically for model performance that this research will address. An interdisciplinary research team will be able to apply the methods developed to identify key features, redundant features, compact models, and so forth, with statistical validity. The National Science Foundation areas of emphasis: earth systems, dynamics of coupled natural and human systems, materials use, and ecology of infectious diseases, provide examples, and complex manufacturing, supply chains, design optimizations, and transportation provide additional examples. Rather than limited data assumptions, the methods are to be developed to apply in the context of high-dimensional, dirty, redundant, missing, mixed data and nonlinear, interactive models that such systems often require.