short-paper

Programming-by-example for data transformation to improve machine learning performance

Authors:

Minori Narita,

Takeo IgarashiAuthors Info & Claims

IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 113 - 114

https://doi.org/10.1145/3308557.3308683

Published: 16 March 2019 Publication History

Get Access

Abstract

In this study, we propose a programming-by-example (PBE)-based data transformation method for feature engineering in machine learning. Data transformation by PBE is not new. However, we utilized the one proposed herein to improve the performance of machine learning in synthesizing a transformation rule from examples. Herein, the system first generates candidate rules, and then chooses the rule that achieves the highest performance in a target machine learning task. We tested this system with the Titanic dataset, and the result shows that the proposed method can avoid worst-case performance compared to the original PBE method.

References

[1]

Sumit Gulwani, William R. Harris, and Rishabh Singh. 2012. Spreadsheet data manipulation using examples. Commun. ACM 55, 8 (2012).

Digital Library

Google Scholar

[2]

Zhongjun Jin, Michael R. Anderson, Michael Cafarella, and Hosagrahar V. Jagadish. 2017. Foofah: A programming-by-example system for synthesizing data transformation programs. In ACM SIGMOD. 1607--1610.

Digital Library

Google Scholar

[3]

James Max Kanter and Kalyan Veeramachaneni. 2015. Deep feature synthesis: Towards automating data science endeavors. In DSAA.

Google Scholar

[4]

David Kofoed Wind. 2014. Concepts in predictive machine learning. Master's thesis. Technical Univ. of Denmark, Denmark.

Google Scholar

Cited By

View all

Sallah AAbdellaoui Alaoui EAgoujil SNayyar A(2022)Machine Learning Interpretability to Detect Fake Accounts in InstagramInternational Journal of Information Security and Privacy10.4018/IJISP.30366516:1(1-25)Online publication date: 13-Jul-2022
https://doi.org/10.4018/IJISP.303665
Park JKim S(2020)Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure InformationInternational Journal of Environmental Research and Public Health10.3390/ijerph1718657317:18(6573)Online publication date: 9-Sep-2020
https://doi.org/10.3390/ijerph17186573
Tang H(2020)Literature Search and ReviewEngineering Research10.1002/9781119624547.ch3(85-125)Online publication date: 11-Dec-2020
https://doi.org/10.1002/9781119624547.ch3

Index Terms

Programming-by-example for data transformation to improve machine learning performance
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
      1. User interface programming

Recommendations

Data-centric disambiguation for data transformation with programming-by-example
IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Programming-by-example (PBE), can be a powerful tool to reduce manual work in repetitive data transformation tasks. However, few examples often leave ambiguity and may cause undesirable data transformation by the system. This ambiguity can be resolved ...
Foofah: A Programming-By-Example System for Synthesizing Data Transformation Programs
SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data

Advancements in new data analysis and visualization technologies have resulted in wide applicability of data-driven decision making. However, raw data from various sources must be wrangled into a suitable form before they are processed by the downstream ...
FlashAttention: Data-centric Interaction for Data Transformation Using Programming-by-Example
UIST '20 Adjunct: Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

Programming-by-example (PBE) can be a powerful tool for reducing manual work in repetitive data transformation tasks. However, having few examples often leaves ambiguity and may cause undesirable data transformation by the system. This ambiguity can be ...

Comments

Information & Contributors

Information

Published In

IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

173 pages

ISBN:9781450366731

DOI:10.1145/3308557

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

JST CREST

Conference

IUI '19

Sponsor:

IUI '19: 24th International Conference on Intelligent User Interfaces

March 16 - 20, 2019

California, Marina del Ray

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
109
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sallah AAbdellaoui Alaoui EAgoujil SNayyar A(2022)Machine Learning Interpretability to Detect Fake Accounts in InstagramInternational Journal of Information Security and Privacy10.4018/IJISP.30366516:1(1-25)Online publication date: 13-Jul-2022
https://doi.org/10.4018/IJISP.303665
Park JKim S(2020)Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure InformationInternational Journal of Environmental Research and Public Health10.3390/ijerph1718657317:18(6573)Online publication date: 9-Sep-2020
https://doi.org/10.3390/ijerph17186573
Tang H(2020)Literature Search and ReviewEngineering Research10.1002/9781119624547.ch3(85-125)Online publication date: 11-Dec-2020
https://doi.org/10.1002/9781119624547.ch3

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Data-centric disambiguation for data transformation with programming-by-example

Foofah: A Programming-By-Example System for Synthesizing Data Transformation Programs

FlashAttention: Data-centric Interaction for Data Transformation Using Programming-by-Example

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations