Evaluating GAM-Like Neural Network Architectures for Interpretable Machine Learning

Booker, William

Evaluating GAM-Like Neural Network Architectures for Interpretable Machine Learning

dc.contributor.advisor	McGovern, Amy
dc.contributor.author	Booker, William
dc.contributor.committeeMember	Hougen, Dean
dc.contributor.committeeMember	Grant, Christan
dc.date.accessioned	2019-05-10T18:30:40Z
dc.date.available	2019-05-10T18:30:40Z
dc.date.issued	2019-05
dc.date.manuscript	2019-05
dc.description.abstract	In many machine learning applications, interpretability is of the utmost importance. Artificial intelligence is proliferating, but before you entrust your finances, your well-being, or even your life to a machine, you’d really like to be sure that it knows what it’s doing. As a human, the best way to evaluate an algorithm is to pick it apart, understand how it works, and figure out how it arrives at the decisions it does. Unfortunately, as machine learning techniques become more powerful and more complicated, reverse-engineering is becoming more difficult. Engineers often choose to implement a model that is accurate rather than one that understandable. In this work, we demonstrate a novel technique that, in certain circumstances, can be both. This work introduces a novel neural network architecture that improves interpretability without sacrificing model accuracy. We test this architecture in a number of real-world classification datasets and demonstrate that it performs almost identically to state-of-the-art methods. We introduce Pandemic, a novel image classification benchmark, to demonstrate that our architecture has further applications in deep-learning models.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/319701
dc.language	en_US	en_US
dc.rights	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	GAM	en_US
dc.subject	Machine Learning	en_US
dc.subject	Interpretability	en_US
dc.subject	Neural Networks	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Evaluating GAM-Like Neural Network Architectures for Interpretable Machine Learning	en_US
ou.group	Gallogly College of Engineering::School of Computer Science	en_US