Standard measure and SVM measure for feature selection and their performance effect for text classification

Yusuke Adachi; Naoya Onimura; Takanori Yamashita; Sachio Hirokawa

doi:10.1145/3011141.3011190

Standard measure and SVM measure for feature selection and their performance effect for text classification

Yusuke Adachi, Naoya Onimura, Takanori Yamashita, Sachio Hirokawa

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

This paper compares the prediction performance of document classification based on a variety of feature selection measures. Empirical experiments were conducted for the dataset re0 with 10 measures for feature selection and with SVM. It is confirmed that the feature selection based on the SVM-score proposed by Sakai and Hirokawa (2012) outper-forms the standard measures with small number of features. In fact, 100 words are enough to get the similar performance obtained with all words. The reason of good performance of this feature selection is that the SVM-score capture not only the characteristic words of positive samples but of negative samples as well.

Original language	English
Title of host publication	18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
Editors	Maria Indrawan-Santiago, Gabriele Anderst-Kotsis, Matthias Steinbauer, Ismail Khalil
Publisher	Association for Computing Machinery
Pages	262-266
Number of pages	5
ISBN (Electronic)	9781450348072
DOIs	https://doi.org/10.1145/3011141.3011190
Publication status	Published - Nov 28 2016
Event	18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Singapore, Singapore Duration: Nov 28 2016 → Nov 30 2016

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016
Country/Territory	Singapore
City	Singapore
Period	11/28/16 → 11/30/16

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3011141.3011190

Cite this

Adachi, Y., Onimura, N., Yamashita, T., & Hirokawa, S. (2016). Standard measure and SVM measure for feature selection and their performance effect for text classification. In M. Indrawan-Santiago, G. Anderst-Kotsis, M. Steinbauer, & I. Khalil (Eds.), 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings (pp. 262-266). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3011141.3011190

Standard measure and SVM measure for feature selection and their performance effect for text classification. / Adachi, Yusuke; Onimura, Naoya; Yamashita, Takanori et al.
18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings. ed. / Maria Indrawan-Santiago; Gabriele Anderst-Kotsis; Matthias Steinbauer; Ismail Khalil. Association for Computing Machinery, 2016. p. 262-266 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Adachi, Y, Onimura, N, Yamashita, T & Hirokawa, S 2016, Standard measure and SVM measure for feature selection and their performance effect for text classification. in M Indrawan-Santiago, G Anderst-Kotsis, M Steinbauer & I Khalil (eds), 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 262-266, 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016, Singapore, Singapore, 11/28/16. https://doi.org/10.1145/3011141.3011190

Adachi Y, Onimura N, Yamashita T, Hirokawa S. Standard measure and SVM measure for feature selection and their performance effect for text classification. In Indrawan-Santiago M, Anderst-Kotsis G, Steinbauer M, Khalil I, editors, 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings. Association for Computing Machinery. 2016. p. 262-266. (ACM International Conference Proceeding Series). doi: 10.1145/3011141.3011190

Adachi, Yusuke ; Onimura, Naoya ; Yamashita, Takanori et al. / Standard measure and SVM measure for feature selection and their performance effect for text classification. 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings. editor / Maria Indrawan-Santiago ; Gabriele Anderst-Kotsis ; Matthias Steinbauer ; Ismail Khalil. Association for Computing Machinery, 2016. pp. 262-266 (ACM International Conference Proceeding Series).

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Standard measure and SVM measure for feature selection and their performance effect for text classification

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