Active Learning with Combinatorial Coverage

Active learning is a practical field of machine learning as labeling data or determining which data to label can be a time consuming and inefficient task. Active learning automates the process of selecting which data to label, but current methods are heavily model reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing Combinatorial Coverage to overcome these issues. The proposed methods are data-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to different models and has a competitive sampling bias compared to benchmark methods.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Katragadda, Sai Prathyush

Chair dc:contributor.committeechair

• Beling, Peter A.

Committee members dc:contributor.committeemember

• Bansal, Manish
• Freeman, Laura J.

• Active Learning
• Combinatorial Interaction Testing
• Combinatorial Coverage

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35378

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111467