A generalized hierarchical approach for data labeling

The goal of this thesis was to develop a data type agnostic classification algorithm best suited for problems where there are a large number of similar labels (e.g., classifying a port versus a shipyard). The most common approach to this issue is to simply ignore it, and attempt to fit a classifier against all targets at once (a "flat" classifier). The problem with this technique is that it tends to do poorly due to label similarity. Conversely, there are other existing approaches, known as hierarchical classifiers (HCs), which propose clustering heuristics to group the labels. However, the most common HCs require that a "flat" model be trained a-priori before the label hierarchy can be learned. The primary issue with this approach is that if the initial estimator performs poorly then the resulting HC will have a similar rate of error.

Name thesis:degree_name

Master

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

• Blanks, Zachary D.

Advisor dc:contributor.advisor

• Troy M. Lau and Rahul Mazumder.

• 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

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122386

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122386