ARDA : automatic relational data augmentation for machine learning

This thesis is motivated by two major trends in data science: easy access to tremendous amounts of unstructured data and the effectiveness of Machine Learning (ML) in data driven applications. As a result, there is a growing need to integrate ML models and data curation into a homogeneous system such that the model informs the choice and extent of data curation. The bottleneck in designing such a system is to efficiently discern what additional information would result in improving the generalization of the ML models. We design an end-to-end system that takes as input a data set, a ML model and access to unstructured data, and outputs an augmented data set such that training the model on this dataset results in better generalization error. Our system has two distinct components: 1) a framework to search and join unstructured data with the input data, based on various attributes of the input and 2) an efficient feature selection algorithm that prunes our noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of system and benchmark our feature selection algorithm with existing state-of-the-art algorithms on numerous real-world datasets.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Chepurko, Nadiia.

Advisor dc:contributor.advisor

• David R. Karger.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

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/1721.1/128413

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128413