demonstration

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FeatureMiner: A Tool for Interactive Feature Selection

Authors:

Kewei Cheng,

Jundong Li,

Huan LiuAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2445 - 2448

https://doi.org/10.1145/2983323.2983329

Published: 24 October 2016 Publication History

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Abstract

The recent popularity of big data has brought immense quantities of high-dimensional data, which presents challenges to traditional data mining tasks due to curse of dimensionality. Feature selection has shown to be effective to prepare these high dimensional data for a variety of learning tasks. To provide easy access to feature selection algorithms, we provide an interactive feature selection tool FeatureMiner based on our recently released feature selection repository scikit-feature. FeatureMiner eases the process of performing feature selection for practitioners by providing an interactive user interface. Meanwhile, it also gives users some practical guidance in finding a suitable feature selection algorithm among many given a specific dataset. In this demonstration, we show (1) How to conduct data preprocessing after loading a dataset; (2) How to apply feature selection algorithms; (3) How to choose a suitable algorithm by visualized performance evaluation.

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Cited By

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Naik AKuppili V(2024)An embedded feature selection method based on generalized classifier neural network for cancer classificationComputers in Biology and Medicine10.1016/j.compbiomed.2023.107677168:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107677
He MZhang CLin JXu JLin WPan Z(2022)CmpQTS: Comparative Visual Analysis of Quantitative Timing Strategies2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927907(1-7)Online publication date: 12-Sep-2022
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927907
Yue XGu QWang DQu HWang Y(2021)iQUANT: Interactive Quantitative Investment Using Sparse Regression FactorsComputer Graphics Forum10.1111/cgf.1429940:3(189-200)Online publication date: 29-Jun-2021
https://doi.org/10.1111/cgf.14299
Show More Cited By

Index Terms

FeatureMiner: A Tool for Interactive Feature Selection
1. Human-centered computing
  1. Interaction design
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

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Demonstration

Funding Sources

National Science Foundation

Conference

CIKM'16

Sponsor:

CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

View all

Naik AKuppili V(2024)An embedded feature selection method based on generalized classifier neural network for cancer classificationComputers in Biology and Medicine10.1016/j.compbiomed.2023.107677168:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107677
He MZhang CLin JXu JLin WPan Z(2022)CmpQTS: Comparative Visual Analysis of Quantitative Timing Strategies2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927907(1-7)Online publication date: 12-Sep-2022
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927907
Yue XGu QWang DQu HWang Y(2021)iQUANT: Interactive Quantitative Investment Using Sparse Regression FactorsComputer Graphics Forum10.1111/cgf.1429940:3(189-200)Online publication date: 29-Jun-2021
https://doi.org/10.1111/cgf.14299
Nie FYang SZhang RLi X(2019)A General Framework for Auto-Weighted Feature Selection via Global Redundancy MinimizationIEEE Transactions on Image Processing10.1109/TIP.2018.288676128:5(2428-2438)Online publication date: May-2019
https://doi.org/10.1109/TIP.2018.2886761
Li JCheng KWang SMorstatter FTrevino RTang JLiu H(2017)Feature SelectionACM Computing Surveys10.1145/313662550:6(1-45)Online publication date: 6-Dec-2017
https://dl.acm.org/doi/10.1145/3136625
Cheng KLi JLiu HMatwin SYu SFarooq F(2017)Unsupervised Feature Selection in Signed Social NetworksProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098106(777-786)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098106

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