Topological Data Analysis in Conjunction with Traditional Machine Learning Techniques to Predict MDAP PM Ratings
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Authors
Joseph, Brian B.
Pham, Trami
Hastings, Christopher
Subjects
Advisors
Date of Issue
2021-05-20
Date
05/20/21
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Topological data analysis (TDA) is an unconventional machine learning technique that is used to understand the underlying topology of data. The premise is that data has shape. The two methodologies used in TDA are persistent homology and the mapper algorithm. Traditional machine learning techniques include supervised unsupervised methods such as clustering, Bayesian networks, neural networks, support vector machines (SVM), and random forests. The goal of this study is to apply TDA methods in conjunction with traditional machine learning algorithms to Defense Acquisition Executive Summary (DAES) data to determine if TDA helps to improve prediction measures (accuracy, f-measure, sensitivity, and specificity) over using traditional methods only when predicting program manager ratings from Major Defense Acquisition Programs (MDAPs). We show that TDA when used in conjunction with traditional machine learning models at a local level of the DAES data improved the accuracy of predicting PM cost ratings of MDAPs at 80% of all nodes in training and testing as compared to implementing these models without TDA at the global level.
Type
Presentation
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
SYM-AM-21-148
Sponsors
Prepared for the Naval Postgraduate School, Monterey, CA 93943.
Naval Postgraduate School
Naval Postgraduate School
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.