Discovering Communicable Models from Earth Science Data

Abstract. This chapter describes how we used regression rules to improve upon results previously published in the Earth science literature. In such a scientific application of machine learning, it is crucially important for the learned models to be understandable and communicable. We recount how we selected a learning algorithm to maximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.

• Type: Conference
• Year: 2007
• Where: DIS
• Authors: Mark Schwabacher, Pat Langley, Christopher Potter, Steven A. Klooster, Alicia Torregrosa
• Keywords: DIS 2007, Earth Science Literature, Machine Learning, Regression Rules