ARDA : automatic relational data augmentation for machine learning
Name
1203061775-MIT.pdf
Size
3.92 MB
Format
Adobe PDF
Checksum (MD5)
23d27d8444a8ff654b367b8fe4c713cb
Author(s)
Chepurko, Nadiia.
Advisor(s)
David R. Karger.
Alternative Title
Automatic relational data augmentation for machine learning
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 55-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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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.
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