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Using chi-square statistics to measure similarities for text categorization

Authors: Yao-Tsung Chen,

Meng Chang ChenAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 38, Issue 4

Pages 3085 - 3090

https://doi.org/10.1016/j.eswa.2010.08.100

Published: 01 April 2011 Publication History

Abstract

In this paper, we propose using chi-square statistics to measure similarities and chi-square tests to determine the homogeneity of two random samples of term vectors for text categorization. The properties of chi-square tests for text categorization are studied first. One of the advantages of chi-square test is that its significance level is similar to the miss rate that provides a foundation for theoretical performance (i.e. miss rate) guarantee. Generally a classifier using cosine similarities with TF*IDF performs reasonably well in text categorization. However, its performance may fluctuate even near the optimal threshold value. To improve the limitation, we propose the combined usage of chi-square statistics and cosine similarities. Extensive experiment results verify properties of chi-square tests and performance of the combined usage.

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Chang TLin SHsu MYang M(2022)Incorporating soft information from financial news media for management decisions in dynamic business environmentsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21173243:4(4947-4960)Online publication date: 1-Jan-2022
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Using chi-square statistics to measure similarities for text categorization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 38, Issue 4

April, 2011

1738 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2010.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2011

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Cited By

Bessghaier NSayagh MOuni AMkaouer M(2023)What Constitutes the Deployment and Runtime Configuration System? An Empirical Study on OpenStack ProjectsACM Transactions on Software Engineering and Methodology10.1145/360718633:1(1-37)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1145/3607186
Kumar SMuttoo SSingh V(2022)Classification of Software Defects Using Orthogonal Defect ClassificationInternational Journal of Open Source Software and Processes10.4018/IJOSSP.30074913:1(1-16)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.4018/IJOSSP.300749
Chang TLin SHsu MYang M(2022)Incorporating soft information from financial news media for management decisions in dynamic business environmentsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21173243:4(4947-4960)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-211732
Dogra VVerma SKavita Chatterjee PShafi JChoi JIjaz M(2022)A Complete Process of Text Classification System Using State-of-the-Art NLP ModelsComputational Intelligence and Neuroscience10.1155/2022/18836982022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1883698
Dhar AMukherjee HDash NRoy K(2020)CESS-A System to Categorize Bangla Web Text DocumentsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/339807019:5(1-18)Online publication date: 18-Jun-2020
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Kumar R(2020)Orientation Local Binary Pattern Based Fingerprint MatchingSN Computer Science10.1007/s42979-020-0068-y1:2Online publication date: 10-Feb-2020
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Yelampalli PNayak JGaidhane V(2019)A novel binary feature descriptor to discriminate normal and abnormal chest CT images using dissimilarity measuresPattern Analysis & Applications10.1007/s10044-018-00771-222:4(1517-1526)Online publication date: 1-Nov-2019
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Doganer ACalik S(2017)A New Approach Using Hidden Markov Model and Bayesian Method for Estimate of Word Types in Text MiningInternational Journal of Knowledge and Systems Science10.4018/IJKSS.20171001028:4(17-29)Online publication date: 1-Oct-2017
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Echeverry-Correa JFerreiros-López JCoucheiro-Limeres ACórdoba RMontero J(2015)Topic identification techniques applied to dynamic language model adaptation for automatic speech recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.07.03542:1(101-112)Online publication date: 1-Jan-2015
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Uysal AGunal S(2014)The impact of preprocessing on text classificationInformation Processing and Management: an International Journal10.1016/j.ipm.2013.08.00650:1(104-112)Online publication date: 1-Jan-2014
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