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Towards feature selection in network

Authors:

Jiawei HanAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 1175 - 1184

https://doi.org/10.1145/2063576.2063746

Published: 24 October 2011 Publication History

Abstract

Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). However, in real world, there are tremendous amount of data which are distributing in a network. Existing features selection methods are not suited for networked data because the i.i.d. assumption no longer holds. This motivates us to study feature selection in a network. In this paper, we present a supervised feature selection method based on Laplacian Regularized Least Squares (LapRLS) for networked data. In detail, we use linear regression to utilize the content information, and adopt graph regularization to consider the link information. The proposed feature selection method aims at selecting a subset of features such that the empirical error of LapRLS is minimized. The resultant optimization problem is a mixed integer programming, which is difficult to solve. It is relaxed into a $L_{2,1}$-norm constrained LapRLS problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark networked data sets show that the proposed feature selection method outperforms traditional feature selection method and the state of the art learning in network approaches.

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Cited By

Bhardwaj SBaskar SMarakala VPrasannan Gangodkar DDhole S(2024)FraudGuard Pro: An Ensemble-Based Feature Selection Algorithm for Credit Card Fraud Detection2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724073(1-11)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICCCNT61001.2024.10724073
Xing YPearlson GKochunov PCalhoun VDu Y(2024)Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophreniaNeuroImage10.1016/j.neuroimage.2024.120839(120839)Online publication date: Sep-2024
https://doi.org/10.1016/j.neuroimage.2024.120839
Xie WLi WZhang SWang LYang JZhao D(2022)A novel biomarker selection method combining graph neural network and gene relationships applied to microarray dataBMC Bioinformatics10.1186/s12859-022-04848-y23:1Online publication date: 26-Jul-2022
https://doi.org/10.1186/s12859-022-04848-y
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Index Terms

Towards feature selection in network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

cover image ACM Conferences

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

Copyright © 2011 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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Publication History

Published: 24 October 2011

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CIKM '11

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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

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Cited By

Bhardwaj SBaskar SMarakala VPrasannan Gangodkar DDhole S(2024)FraudGuard Pro: An Ensemble-Based Feature Selection Algorithm for Credit Card Fraud Detection2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724073(1-11)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICCCNT61001.2024.10724073
Xing YPearlson GKochunov PCalhoun VDu Y(2024)Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophreniaNeuroImage10.1016/j.neuroimage.2024.120839(120839)Online publication date: Sep-2024
https://doi.org/10.1016/j.neuroimage.2024.120839
Xie WLi WZhang SWang LYang JZhao D(2022)A novel biomarker selection method combining graph neural network and gene relationships applied to microarray dataBMC Bioinformatics10.1186/s12859-022-04848-y23:1Online publication date: 26-Jul-2022
https://doi.org/10.1186/s12859-022-04848-y
Zhu LHill D(2022)Networked Time Series Shapelet Learning for Power System Transient Stability AssessmentIEEE Transactions on Power Systems10.1109/TPWRS.2021.309342337:1(416-428)Online publication date: Jan-2022
https://doi.org/10.1109/TPWRS.2021.3093423
Li XWang YRuiz R(2022)A Survey on Sparse Learning Models for Feature SelectionIEEE Transactions on Cybernetics10.1109/TCYB.2020.298244552:3(1642-1660)Online publication date: Mar-2022
https://doi.org/10.1109/TCYB.2020.2982445
Ghosh ASenthilrajan A(2022)A Modified Naïve Bayes Classifier for Detecting Spam E-mails based on Feature Selection2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS53718.2022.9788340(1634-1641)Online publication date: 25-May-2022
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Ma YTang J(2021)Deep Learning on Graphs10.1017/9781108924184Online publication date: 2-Sep-2021
https://doi.org/10.1017/9781108924184
He RLiu HZhao L(2021)SCHC: Incorporating Social Contagion and Hashtag Consistency for Topic-Oriented Social SummarizationDatabase Systems for Advanced Applications10.1007/978-3-030-73197-7_44(641-657)Online publication date: 6-Apr-2021
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Peng ZLuo MLi JXue LZheng Q(2020)A Deep Multi-View Framework for Anomaly Detection on Attributed NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3015098(1-1)Online publication date: 2020
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Liu WGong DTan MShi JYang YHauptmann A(2020)Learning Distilled Graph for Large-Scale Social Network Data ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.290406832:7(1393-1404)Online publication date: 1-Jul-2020
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