Article

Combining feature selectors for text classification

Authors:

J. Olsson Scott Olsson,

Douglas W. OardAuthors Info & Claims

CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management

Pages 798 - 799

https://doi.org/10.1145/1183614.1183736

Published: 06 November 2006 Publication History

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Abstract

We introduce several methods of combining feature selectors for text classification. Results from a large investigation of these combinations are summarized. Easily constructed combinations of feature selectors are shown to improve peak R-precision and F₁ at statistically significant levels.

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Xiang FZhao YZhang MZuo YZou XTao F(2024)Ensemble learning-based stability improvement method for feature selection towards performance predictionJournal of Manufacturing Systems10.1016/j.jmsy.2024.03.00174(55-67)Online publication date: Jun-2024
https://doi.org/10.1016/j.jmsy.2024.03.001
Mohanrasu SRakkiyappan R(2024)A novel framework for multi-label feature selection: integrating mutual information and Pythagorean fuzzy CRADISGranular Computing10.1007/s41066-024-00489-z9:3Online publication date: 26-Jun-2024
https://doi.org/10.1007/s41066-024-00489-z
Yang LGuo XChen JWang YMa HLi YYang ZShi Y(2023)Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield MachineChinese Journal of Mechanical Engineering10.1186/s10033-023-00932-336:1Online publication date: 24-Oct-2023
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Index Terms

Combining feature selectors for text classification
1. Information systems
  1. Information retrieval
  2. Information storage systems

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Published In

CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management

November 2006

916 pages

ISBN:1595934332

DOI:10.1145/1183614

General Chair:
Philip S. Yu
IBM T.J. Watson Research Center (USA)
,
Program Chairs:
Vassilis Tsotras
University of California-Riverside (USA)
,
Edward Fox
Virginia Tech (USA)
,
Bing Liu
University of Illinois at Chicago (USA)

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: 06 November 2006

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CIKM06: Conference on Information and Knowledge Management

November 6 - 11, 2006

Virginia, Arlington, USA

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

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Xiang FZhao YZhang MZuo YZou XTao F(2024)Ensemble learning-based stability improvement method for feature selection towards performance predictionJournal of Manufacturing Systems10.1016/j.jmsy.2024.03.00174(55-67)Online publication date: Jun-2024
https://doi.org/10.1016/j.jmsy.2024.03.001
Mohanrasu SRakkiyappan R(2024)A novel framework for multi-label feature selection: integrating mutual information and Pythagorean fuzzy CRADISGranular Computing10.1007/s41066-024-00489-z9:3Online publication date: 26-Jun-2024
https://doi.org/10.1007/s41066-024-00489-z
Yang LGuo XChen JWang YMa HLi YYang ZShi Y(2023)Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield MachineChinese Journal of Mechanical Engineering10.1186/s10033-023-00932-336:1Online publication date: 24-Oct-2023
https://doi.org/10.1186/s10033-023-00932-3
Sathya PGnanasekaran P(2023)Ensemble Feature Selection Framework for Paddy Yield Prediction in Cauvery Basin using Machine Learning ClassifiersCogent Engineering10.1080/23311916.2023.225006110:2Online publication date: 29-Aug-2023
https://doi.org/10.1080/23311916.2023.2250061
Balakrishnan KDhanalakshmi R(2022)Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions微阵列数据集的特征选择技术: 综合评述、分类和未来方向Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.210056923:10(1451-1478)Online publication date: 24-Oct-2022
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Li MQin YZhou H(2022)A DDoS Detection Method with Feature Set Dimension ReductionMobile Internet Security10.1007/978-981-16-9576-6_25(365-378)Online publication date: 22-Jan-2022
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Miri MDowlatshahi MHashemi ARafsanjani MGupta BAlhalabi W(2022)Ensemble feature selection for multi‐label text classification: An intelligent order statistics approachInternational Journal of Intelligent Systems10.1002/int.2304437:12(11319-11341)Online publication date: 2-Sep-2022
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Xu HZhu MXiao BQiu Y(2021)A Novel Fault Diagnosis Method Based on Ensemble Feature Selection in The Industrial IoT Scenario2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9658901(3324-3329)Online publication date: 17-Oct-2021
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Shinkuma RNishio TInagaki YOki E(2020)Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learningEURASIP Journal on Wireless Communications and Networking10.1186/s13638-020-01709-12020:1Online publication date: 11-May-2020
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Ensemble feature selection for single-label text classification: a comprehensive analytical study

Feature selection for text classification with Naïve Bayes

High-performing feature selection for text classification

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