Article

Generalized LARS as an effective feature selection tool for text classification with SVMs

Author:

S. Sathiya KeerthiAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 417 - 424

https://doi.org/10.1145/1102351.1102404

Published: 07 August 2005 Publication History

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Abstract

In this paper we generalize the LARS feature selection method to the linear SVM model, derive an efficient algorithm for it, and empirically demonstrate its usefulness as a feature selection tool for text classification.

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Shaer LKanj RJoshi R(2023)A Best Balance Ratio Ordered Feature Selection Methodology for Robust and Fast Statistical Analysis of Memory DesignsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.321376242:6(1742-1755)Online publication date: Jun-2023
https://doi.org/10.1109/TCAD.2022.3213762
Yang SLiu YZhang YZhu J(2022)A Word-Concept Heterogeneous Graph Convolutional Network for Short Text ClassificationNeural Processing Letters10.1007/s11063-022-10906-655:1(735-750)Online publication date: 22-Jun-2022
https://doi.org/10.1007/s11063-022-10906-6
Mourya A(2019)Performance and Evaluation of Support Vector Machine and Artificial Neural Network over Heterogeneous DataRecent Trends in Image Processing and Pattern Recognition10.1007/978-981-13-9181-1_51(584-595)Online publication date: 20-Jul-2019
https://doi.org/10.1007/978-981-13-9181-1_51
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Generalized LARS as an effective feature selection tool for text classification with SVMs
1. Computing methodologies
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Information

Published In

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Shaer LKanj RJoshi R(2023)A Best Balance Ratio Ordered Feature Selection Methodology for Robust and Fast Statistical Analysis of Memory DesignsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.321376242:6(1742-1755)Online publication date: Jun-2023
https://doi.org/10.1109/TCAD.2022.3213762
Yang SLiu YZhang YZhu J(2022)A Word-Concept Heterogeneous Graph Convolutional Network for Short Text ClassificationNeural Processing Letters10.1007/s11063-022-10906-655:1(735-750)Online publication date: 22-Jun-2022
https://doi.org/10.1007/s11063-022-10906-6
Mourya A(2019)Performance and Evaluation of Support Vector Machine and Artificial Neural Network over Heterogeneous DataRecent Trends in Image Processing and Pattern Recognition10.1007/978-981-13-9181-1_51(584-595)Online publication date: 20-Jul-2019
https://doi.org/10.1007/978-981-13-9181-1_51
Kang D(2011)Data-Driven Evaluation of Ontologies Using Machine Learning AlgorithmsApplied Semantic Web Technologies10.1201/b11085-13(211-273)Online publication date: 10-Aug-2011
https://doi.org/10.1201/b11085-13
He XBeauseroy PSmolarz A(2010)Nearest-neighbour ensembles in lasso feature subspacesIET Computer Vision10.1049/iet-cvi.2009.00564:4(306)Online publication date: 2010
https://doi.org/10.1049/iet-cvi.2009.0056
Loosli GCanu S(2010)Quadratic Programming and Machine Learning — Large Scale Problems and SparsityOptimization in Signal and Image Processing10.1002/9780470611319.ch5(111-135)Online publication date: 27-Jan-2010
https://doi.org/10.1002/9780470611319.ch5
Li XDai HWang M(2009)Two-Stage Feature Selection Method for Text ClassificationProceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 0110.1109/MINES.2009.127(234-238)Online publication date: 18-Nov-2009
https://dl.acm.org/doi/10.1109/MINES.2009.127
Csorba KVajk I(2008)Cascaded search for similar documents between mobile devicesProceedings of the 12th WSEAS international conference on Computers10.5555/1513605.1513630(122-127)Online publication date: 23-Jul-2008
https://dl.acm.org/doi/10.5555/1513605.1513630
Ye JChen KWu TLi JZhao ZPatel RBae MJanardan RLiu HAlexander GReiman ELi YLiu BSarawagi S(2008)Heterogeneous data fusion for alzheimer's disease studyProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1401890.1402012(1025-1033)Online publication date: 24-Aug-2008
https://dl.acm.org/doi/10.1145/1401890.1402012
Csorba KVajk I(2008)Taxonomic support for document classication in mobile device environment2008 Conference on Human System Interactions10.1109/HSI.2008.4581521(674-679)Online publication date: May-2008
https://doi.org/10.1109/HSI.2008.4581521
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