On the redundancy in large material datasets: efficient and robust learning with less data Article Swipe
Kangming Li
,
Daniel Persaud
,
Kamal Choudhary
,
Brian DeCost
,
Michael Greenwood
,
Jason Hattrick‐Simpers
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.13076
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.13076
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95 % of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.13076
- https://arxiv.org/pdf/2304.13076
- OA Status
- green
- Cited By
- 6
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367189432
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https://openalex.org/W4367189432Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.13076Digital Object Identifier
- Title
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On the redundancy in large material datasets: efficient and robust learning with less dataWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-25Full publication date if available
- Authors
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Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael Greenwood, Jason Hattrick‐SimpersList of authors in order
- Landing page
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https://arxiv.org/abs/2304.13076Publisher landing page
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https://arxiv.org/pdf/2304.13076Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2304.13076Direct OA link when available
- Concepts
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Redundancy (engineering), Computer science, Robustness (evolution), Machine learning, Training set, Artificial intelligence, Data mining, Gene, Chemistry, Biochemistry, Operating systemTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 1, 2023: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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