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ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning

Authors:

Sanjay Krishnan,

Michael J. Franklin,

Eugene WuAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 2117 - 2120

https://doi.org/10.1145/2882903.2899409

Published: 26 June 2016 Publication History

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.

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Cited By

Sindhu PPeruri GYalavarthi M(2024)An empirically based object-oriented testing using Machine learningEAI Endorsed Transactions on Internet of Things10.4108/eetiot.534410Online publication date: 8-Mar-2024
https://doi.org/10.4108/eetiot.5344
(2024)Work order prioritization using neural networks to improve building operationJournal of Information Technology in Construction10.36680/j.itcon.2024.01629(324-346)Online publication date: 18-Apr-2024
https://doi.org/10.36680/j.itcon.2024.016
Shah VParashos TKumar A(2024)How Do Categorical Duplicates Affect ML? A New Benchmark and Empirical AnalysesProceedings of the VLDB Endowment10.14778/3648160.364817817:6(1391-1404)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648178
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Index Terms

ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data cleaning

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Published In

cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 26 June 2016

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SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Sindhu PPeruri GYalavarthi M(2024)An empirically based object-oriented testing using Machine learningEAI Endorsed Transactions on Internet of Things10.4108/eetiot.534410Online publication date: 8-Mar-2024
https://doi.org/10.4108/eetiot.5344
(2024)Work order prioritization using neural networks to improve building operationJournal of Information Technology in Construction10.36680/j.itcon.2024.01629(324-346)Online publication date: 18-Apr-2024
https://doi.org/10.36680/j.itcon.2024.016
Shah VParashos TKumar A(2024)How Do Categorical Duplicates Affect ML? A New Benchmark and Empirical AnalysesProceedings of the VLDB Endowment10.14778/3648160.364817817:6(1391-1404)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648178
Hu GZeng XYu WPeng MYuan MDuan L(2024)Unsupervised and Supervised Co-learning for Comment-based Codebase Refining and its Application in Code SearchProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686664(1-12)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686664
Ahmad EGeorge VSoman DKanthaliya RKumar DPateriya V(2024)Integrated Tool for Cleaning Bulk Solar Power Data2024 Second International Conference on Smart Technologies for Power and Renewable Energy (SPECon)10.1109/SPECon61254.2024.10537256(1-5)Online publication date: 2-Apr-2024
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Gonzalez Santacruz ERomero DNoguez JWuest T(2024)Integrated quality 4.0 framework for quality improvement based on Six Sigma and machine learning techniques towards zero-defect manufacturingThe TQM Journal10.1108/TQM-11-2023-0361Online publication date: 28-Mar-2024
https://doi.org/10.1108/TQM-11-2023-0361
Krichen MAbdalzaher M(2024)Performance enhancement of artificial intelligence: A surveyJournal of Network and Computer Applications10.1016/j.jnca.2024.104034232(104034)Online publication date: Dec-2024
https://doi.org/10.1016/j.jnca.2024.104034
Tawakuli AHavers BGulisano VKaiser DEngel T(2024)Survey:Time-series data preprocessing: A survey and an empirical analysisJournal of Engineering Research10.1016/j.jer.2024.02.018Online publication date: Mar-2024
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Krafft THauer MZweig K(2024)Black-Box Testing and Auditing of Bias in ADM SystemsMinds and Machines10.1007/s11023-024-09666-034:2Online publication date: 25-May-2024
https://doi.org/10.1007/s11023-024-09666-0
Goyle KXie QGoyle V(2024)DataAssist: A Machine Learning Approach to Data Cleaning and PreparationIntelligent Systems and Applications10.1007/978-3-031-66431-1_33(476-486)Online publication date: 31-Jul-2024
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