The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Deep feature synthesis: Towards automating data science endeavors
Abstract
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically. To achieve this automation, we first propose and develop the Deep Feature Synthesis algorithm for automatically generating features for relational datasets. The algorithm follows relationships in the data to a base field, and then sequentially applies mathematical functions along that path to create the final feature. Second, we implement a generalizable machine learning pipeline and tune it using a novel Gaussian Copula process based approach. We entered the Data Science Machine in 3 data science competitions that featured 906 other data science teams. Our approach beats 615 teams in these data science competitions. In 2 of the 3 competitions we beat a majority of competitors, and in the third, we achieved 94% of the best competitor's score. In the best case, with an ongoing competition, we beat 85.6% of the teams and achieved 95.7% of the top submissions score.
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Cites
Histograms of Oriented Gradients for Human Detection
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Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
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Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359313 · Cited by 342
Theory-Guided Machine Learning in Materials Science
Wagner, N., & Rondinelli, J. M. (2016) · Frontiers in Materials · doi:10.3389/fmats.2016.00028 · Cited by 188
Drozdal, J., Weisz, J., Wang, D., Dass, G., Yao, B., Zhao, C., Muller, M., Ju, L., & Su, H. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377501 · Cited by 119
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Metadata
- Authors
- Kanter, J. M., & Veeramachaneni, K.
- Venue
- 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 1-10
- OpenAlex
- W2182353144
- Cited by
- 421
- References
- 5
- FWCI
- 9.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kanter, J. M., & Veeramachaneni, K. (2015). Deep feature synthesis: Towards automating data science endeavors. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). https://doi.org/10.1109/dsaa.2015.7344858
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