SGER: Exploiting Contextual Knowledge to Design Input Representations for Machine Learning

非专业人员通常很难设计用于制定机器学习问题的功能。这项探索性研究的小额资助将探索机器学习问题的“特征工程”方面自动化的可能性。具体而言，本项目将研究用现代知识表示语言表示背景知识，然后自动（或半自动）导出相关输入特征的可行性。该项目将研究两个有广泛背景知识但缺乏训练数据的应用问题：（a）预测今后的蝗虫灾害;（B）了解西尼罗河病毒的传播。这项研究将提供一个更深入的了解过程中的特征工程，并有助于阐明议程，为未来的研究。

Non-specialists often have difficulty designing the features used to formulate machine learning problems. This small grant for exploratory research will explore the possibility of automating this "feature engineering" aspect of machine learning problems. Specifically, this project will investigate the feasibility of representing the background knowledge in a modern knowledge representation language and then automatically (or semi-automatically) deriving the relevant input features. The project will study two application problems where there is extensive background knowledge but sparse training data: (a) predicting future grasshopper infestations; and (b) understanding the spread of West Nile virus. This research will provide a deeper understanding of the process of feature engineering and help articulate an agenda for future research.