ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning

By Sanjay Krishnan, M. Franklin, Ken Goldberg, Jiannan Wang, Eugene Wu. 2016

71 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Evaluation & Benchmarks, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sanjay Krishnan
2. Eugene Wu
3. M. Franklin
4. Ken Goldberg
5. Jiannan Wang
6. Ihab F. Ilyas
7. Xin He
8. Philipp Schmidt

In Management Science and Operations Research

1. Sanjay Krishnan
2. Eugene Wu
3. M. Franklin
4. Jiannan Wang
5. Ken Goldberg
6. Xin He
7. Philipp Schmidt
8. Kaiyong Zhao

In Evaluation & Benchmarks

1. Sanjay Krishnan
2. Eugene Wu
3. Jiannan Wang
4. Ken Goldberg
5. M. Franklin
6. Xin He
7. Kaiyong Zhao
8. Xiaowen Chu

In Human-Computer Interaction

1. Ken Goldberg
2. Philipp Schmidt
3. Kaiyong Zhao
4. Felix Biessmann
5. Eugene Wu

Citations in the corpus

• AutoML: A Survey of the State-of-the-Art (2019, background): “As an alternative, some studies propose possible automated data-cleaning processes [78, 79, 80, 81].”
• BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Software Engineers must constantly monitor and maintain the data processing pipeline to account for unexpected changes [30,47].”
• Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, neutral): “ata and protocols is rising [16, 19]. There are also intersecting literatures on systems for capturing information in ML data flows and supply chains [15, 54, 60], as well as supporting data cleaning [31, 55]. These issues have long been discussed in the fields of library and information science, pa”
• ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, methodology): “We have included detailed proofs in our extended technical report [24] and an implementation of ActiveClean in our SIGMOD demo [21].”
• Automating Large-Scale Data Quality Verification (2018, methodology): “ActiveClean similarly uses active learning for prioritization, but at the same time it learns and updates a convex loss model [24].”