Neural Shrubs

• Activity Number: 525 - Contributed Poster Presentations: Section on Statistics in Defense and National Security
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Kyle Caudle* and Randy A Hoover
• Companies: South Dakota School of Mines and Technology and South Dakota School of Mines and Technology
• Keywords: Decision Trees; Neural Networks

Abstract

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that increase the predictive capability of the tree by between 5 and 10%.