ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning

ABSTRACT

Databases can be corrupted with various errors such as missing, incorrect, or inconsistent values. Increasingly, modern data analysis pipelines involve Machine Learning, and the effects of dirty data can be difficult to debug.Dirty data is often sparse, and naive sampling solutions are not suited for high-dimensional models. We propose ActiveClean, a progressive framework for training Machine Learning models with data cleaning. Our framework updates a model iteratively as the analyst cleans small batches of data, and includes numerous optimizations such as importance weighting and dirty data detection. We designed a visual interface to wrap around this framework and demonstrate ActiveClean for a video classification problem and a topic modeling problem.

References

1. U. Berkeley. Berkeley data analytics stack. https://amplab.cs.berkeley.edu/software/.
2. A. Crotty, A. Galakatos, and T. Kraska. Tupleware: Distributed machine learning on small clusters. In IEEE Data Eng. Bull., 2014.
3. C. Gokhale, S. Das, A. Doan, J. F. Naughton, N. Rampalli, J. Shavlik, and X. Zhu. Corleone: Hands-off crowdsourcing for entity matching. In SIGMOD, 2014.
4. D. Haas, S. Krishnan, J. Wang, M. J. Franklin, and E. Wu. Wisteria: Nurturing scalable data cleaning infrastructure. In VLDB, 2015.
5. G. Inc. Tensorflow. https://www.tensorflow.org/.
6. S. Krishnan, J. Wang, M. J. Franklin, K. Goldberg, and T. Kraska. Stale view cleaning: Getting fresh answers from stale materialized views. In VLDB, 2015.
7. S. Krishnan, J. Wang, M. J. Franklin, K. Goldberg, T. Kraska, T. Milo, and E. Wu. Sampleclean: Fast and reliable analytics on dirty data. IEEE Data Eng. Bull., 38(3):59--75, 2015.
8. S. Krishnan, J. Wang, E. Wu, M. J. Franklin, and K. Goldberg. Activeclean: Interactive data cleaning while learning convex loss models. http://arxiv.org/abs/1601.03797v1.
9. J. Mahler, S. Krishnan, M. Laskey, S. Sen, A. Murali, B. Kehoe, S. Patil, J. Wang, M. Franklin, P. Abbeel, and K. Y. Goldberg. Learning accurate kinematic control of cable-driven surgical robots using data cleaning and gaussian process regression. In CASE, 2014.
10. P. Spark. Pyspark. https://spark.apache.org/docs/0.9.0/python-programming-guide.html.
11. L. Van der Maaten and G. Hinton. Visualizing data using t-sne. In JMLR, 2008.
12. J. Wang, S. Krishnan, M. J. Franklin, K. Goldberg, T. Kraska, and T. Milo. A sample-and-clean framework for fast and accurate query processing on dirty data. In SIGMOD, 2014.
13. H. Xiao, B. Biggio, G. Brown, G. Fumera, C. Eckert, and F. Roli. Is feature selection secure against training data poisoning? In ICML, 2015.
14. M. Yakout, L. Berti-Equille, and A. K. Elmagarmid. Don't be scared: use scalable automatic repairing with maximal likelihood and bounded changes. In SIGMOD, 2013.
15. M. Yakout, A. K. Elmagarmid, J. Neville, M. Ouzzani, and I. F. Ilyas. Guided data repair. In VLDB, 2011.

Index Terms

• Information systems

  • Information systems applications

    • Data mining

      • Data cleaning