Skip to main content
SpringerLink
Log in
SpringerLink
Log in

DAFEE: A Scalable Distributed Automatic Feature Engineering Algorithm for Relational Datasets

  • Conference paper
  • First Online:
Algorithms and Architectures for Parallel Processing (ICA3PP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12453))

  • 1908 Accesses

Abstract

Automatic feature engineering aims to construct informative features automatically and reduce manual labor for machine learning applications. The majority of existing approaches are designed to handle tasks with only one data source, which are less applicable to real scenarios. In this paper, we present a distributed automatic feature engineering algorithm, DAFEE, to generate features among multiple large-scale relational datasets. Starting from the target table, the algorithm uses a Breadth-First-Search type algorithm to find its related tables and constructs advanced high-order features that are remarkably effective in practical applications. Moreover, DAFEE implements a feature selection method to reduce the computational cost and improve predictive performance. Furthermore, it is highly optimized to process a massive volume of data. Experimental results demonstrate that it can significantly improve the predictive performance by 7% compared to SOTA algorithms.

W. Zhao and X. Li—These authors contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.dcjingsai.com.

  2. 2.

    http://www.kaggle.com.

  3. 3.

    http://ijcai15.org.

References

  1. Armbrust, M., et al.: Spark SQL: relational data processing in spark. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1383–1394 (2015)

    Google Scholar 

  2. Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5(4), 537–550 (1994)

    Article  Google Scholar 

  3. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

    Google Scholar 

  4. Davis, J.C., Sampson, R.J.: Statistics and Data Analysis in Geology, vol. 646. Wiley, New York (1986)

    Google Scholar 

  5. Dor, O., Reich, Y.: Strengthening learning algorithms by feature discovery. Inf. Sci. 189, 176–190 (2012)

    Article  Google Scholar 

  6. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2012)

    MATH  Google Scholar 

  7. Guo, H., Jack, L.B., Nandi, A.K.: Feature generation using genetic programming with application to fault classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(1), 89–99 (2005)

    Article  Google Scholar 

  8. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)

    Article  Google Scholar 

  9. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507–514 (2006)

    Google Scholar 

  10. He, X., et al.: Practical lessons from predicting clicks on ads at Facebook. In: Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pp. 1–9 (2014)

    Google Scholar 

  11. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration (extended version). Technical report TR-2010-10. Computer Science, University of British Columbia (2010)

    Google Scholar 

  12. Kanter, J.M., Veeramachaneni, K.: Deep feature synthesis: towards automating data science endeavors. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE (2015)

    Google Scholar 

  13. Katz, G., Shin, E.C.R., Song, D.: ExploreKit: automatic feature generation and selection. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 979–984. IEEE (2016)

    Google Scholar 

  14. Kaul, A., Maheshwary, S., Pudi, V.: AutoLearn—automated feature generation and selection. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 217–226. IEEE (2017)

    Google Scholar 

  15. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146–3154 (2017)

    Google Scholar 

  16. Khurana, U., Samulowitz, H., Turaga, D.: Feature engineering for predictive modeling using reinforcement learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  17. Khurana, U., Turaga, D., Samulowitz, H., Parthasrathy, S.: Cognito: automated feature engineering for supervised learning. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 1304–1307. IEEE (2016)

    Google Scholar 

  18. Lam, H.T., Minh, T.N., Sinn, M., Buesser, B., Wistuba, M.: Neural feature learning from relational database. arXiv preprint arXiv:1801.05372 (2018)

  19. Lam, H.T., Thiebaut, J.M., Sinn, M., Chen, B., Mai, T., Alkan, O.: One button machine for automating feature engineering in relational databases. arXiv preprint arXiv:1706.00327 (2017)

  20. Leather, H., Bonilla, E., O’Boyle, M.: Automatic feature generation for machine learning based optimizing compilation. In: 2009 International Symposium on Code Generation and Optimization, pp. 81–91. IEEE (2009)

    Google Scholar 

  21. Lewis, D.D.: Feature selection and feature extraction for text categorization. In: Proceedings of the Workshop on Speech and Natural Language, pp. 212–217. Association for Computational Linguistics (1992)

    Google Scholar 

  22. Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. (CSUR) 50(6), 1–45 (2017)

    Article  Google Scholar 

  23. Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, pp. 388–391. IEEE (1995)

    Google Scholar 

  24. Markovitch, S., Rosenstein, D.: Feature generation using general constructor functions. Mach. Learn. 49(1), 59–98 (2002)

    Article  Google Scholar 

  25. Mitra, P., Murthy, C., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)

    Article  Google Scholar 

  26. Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E.B., Turaga, D.S.: Learning feature engineering for classification. In: IJCAI, pp. 2529–2535 (2017)

    Google Scholar 

  27. Sheikhpour, R., Sarram, M.A., Gharaghani, S., Chahooki, M.A.Z.: A survey on semi-supervised feature selection methods. Pattern Recogn. 64, 141–158 (2017)

    Article  Google Scholar 

  28. Tang, J., Alelyani, S., Liu, H.: Feature selection for classification: a review. In: Data Classification: Algorithms and Applications, p. 37 (2014)

    Google Scholar 

  29. Tran, B., Xue, B., Zhang, M.: Genetic programming for feature construction and selection in classification on high-dimensional data. Memetic Comput. 8(1), 3–15 (2015). https://doi.org/10.1007/s12293-015-0173-y

    Article  Google Scholar 

  30. Yuanfei, L., et al.: AutoCross: automatic feature crossing for tabular data in real-world applications. arXiv preprint arXiv:1904.12857 (2019)

  31. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I., et al.: Spark: cluster computing with working sets. HotCloud 10(10–10), 95 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Transwarp Technology (Shanghai) Co., Ltd., Shanghai, China

    Wenqian Zhao, Xiangxiang Li, Mufeng Lin, Chen Lin & Yifan Yang

  2. The Joint Laboratory of Nanjing University and Transwarp on Data Technology, Nanjing University, Nanjing, China

    Guoping Rong

Authors
  1. Wenqian Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangxiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoping Rong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mufeng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yifan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yifan Yang .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, W., Li, X., Rong, G., Lin, M., Lin, C., Yang, Y. (2020). DAFEE: A Scalable Distributed Automatic Feature Engineering Algorithm for Relational Datasets. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_3

Download citation

Publish with us

Policies and ethics

Search

Navigation