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An iterative voting method based on word density for text classification

Published: 25 May 2011 Publication History

Abstract

In this paper we present an iterative voting (IV) method using the density based weighting for text classification. An in-class word density is used to weight for each word in a topic, so that the word in documents has an array of weights to vote for given topics, and the highest scored topic will be labeled. During the voting process, the iteration strategy is applied for improving the classification effectiveness. This method shows the competitive performance against SVM, NB, KNN, and it has better time efficiency.

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Cited By

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  • (2019)A Method of Short Text Representation Based on the Feature Probability Embedded VectorSensors10.3390/s1917372819:17(3728)Online publication date: 28-Aug-2019
  • (2016)Semi-supervised Locality Preserving Discriminant Analysis for hyperspectral classification2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)10.1109/CISP-BMEI.2016.7852699(151-156)Online publication date: Oct-2016

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cover image ACM Other conferences
WIMS '11: Proceedings of the International Conference on Web Intelligence, Mining and Semantics
May 2011
563 pages
ISBN:9781450301480
DOI:10.1145/1988688
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: 25 May 2011

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Author Tags

  1. classification
  2. iterative voting
  3. word density

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WIMS '11

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Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

View all
  • (2019)A Method of Short Text Representation Based on the Feature Probability Embedded VectorSensors10.3390/s1917372819:17(3728)Online publication date: 28-Aug-2019
  • (2016)Semi-supervised Locality Preserving Discriminant Analysis for hyperspectral classification2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)10.1109/CISP-BMEI.2016.7852699(151-156)Online publication date: Oct-2016

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