Machine learning for problems with missing and uncertain data with applications to personalized medicine

When we try to apply statistical learning in real-world applications, we frequently encounter data which include missing and uncertain values. This thesis explores the problem of learning from missing and uncertain data with a focus on applications in personalized medicine. In the first chapter, we present a framework for classification when data is uncertain that is based upon robust optimization. We show that adding robustness in both the features and labels results in tractable optimization problems for three widely used classification methods: support vector machines, logistic regression, and decision trees. Through experiments on 75 benchmark data sets, we characterize the learning tasks for which adding robustness provides the most value. In the second chapter, we develop a family of methods for missing data imputation based upon predictive methods and formal optimization.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Pawlowski, Colin.

Advisor dc:contributor.advisor

• Dimitris Bertsimas.

• Operations Research Center.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng