Date of Award

3-26-2020

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Jeffrey D. Weir, PhD

Abstract

The importance and value of statistical predictions increase as data grows in availability and quantity. Metamodels, or surrogate models, provide the ability to rapidly approximate and predict information. However, selection of the appropriate metamodel for a given dataset is often arduous, and the choice of the wrong metamodel could lead to considerably inaccurate results. This research proposes and tests the framework for a metamodel recommendation system. The implementation allows for virtually any dataset and preprocesses data, calculates meta-features, evaluates the performance of various metamodels, and learns how the data behaves via meta-learning, thus preparing and bettering itself for future recommendations. Testing on over 500 widely varied datasets, the framework provides positive results, often recommending a metamodel with similar performance as the actual best metamodel.

AFIT Designator

AFIT-ENS-MS-20-M-182

Share

COinS