Iterative evolution of feature space in text classification

Liutao Zhao; Yitian Ren; Bo Yan

doi:10.1109/CISP.2015.7408065

Iterative evolution of feature space in text classification

Liutao Zhao, Yitian Ren, Bo Yan

School of Computer Science and Technology

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

Abstract

Nature language processing is an important part in data mining, which counts a lot in the internet age. Feature extraction effects the accuracy of text classification. This paper proposes a method of iterative feature space evolution to optimize the result. Adjusting the extended dictionary and the stop word list, we optimize the feature space time and again to get a better classifier model. The final result has a higher classification accuracy than the original experiment.

Original language	English
Title of host publication	Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015
Editors	Lipo Wang, Sen Lin, Zhiyong Tao, Bing Zeng, Xiaowei Hui, Liangshan Shao, Jie Liang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1210-1214
Number of pages	5
ISBN (Electronic)	9781467390989
DOIs	https://doi.org/10.1109/CISP.2015.7408065
Publication status	Published - 16 Feb 2016
Event	8th International Congress on Image and Signal Processing, CISP 2015 - Shenyang, China Duration: 14 Oct 2015 → 16 Oct 2015

Publication series

Name	Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015

Conference

Conference	8th International Congress on Image and Signal Processing, CISP 2015
Country/Territory	China
City	Shenyang
Period	14/10/15 → 16/10/15

Keywords

SVM
feature extraction
feature space
text classification

Access to Document

10.1109/CISP.2015.7408065

Cite this

Zhao, L., Ren, Y., & Yan, B. (2016). Iterative evolution of feature space in text classification. In L. Wang, S. Lin, Z. Tao, B. Zeng, X. Hui, L. Shao, & J. Liang (Eds.), Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015 (pp. 1210-1214). Article 7408065 (Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISP.2015.7408065

Zhao, Liutao ; Ren, Yitian ; Yan, Bo. / Iterative evolution of feature space in text classification. Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015. editor / Lipo Wang ; Sen Lin ; Zhiyong Tao ; Bing Zeng ; Xiaowei Hui ; Liangshan Shao ; Jie Liang. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1210-1214 (Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015).

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title = "Iterative evolution of feature space in text classification",

abstract = "Nature language processing is an important part in data mining, which counts a lot in the internet age. Feature extraction effects the accuracy of text classification. This paper proposes a method of iterative feature space evolution to optimize the result. Adjusting the extended dictionary and the stop word list, we optimize the feature space time and again to get a better classifier model. The final result has a higher classification accuracy than the original experiment.",

keywords = "SVM, feature extraction, feature space, text classification",

author = "Liutao Zhao and Yitian Ren and Bo Yan",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 8th International Congress on Image and Signal Processing, CISP 2015 ; Conference date: 14-10-2015 Through 16-10-2015",

year = "2016",

month = feb,

day = "16",

doi = "10.1109/CISP.2015.7408065",

language = "English",

series = "Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1210--1214",

editor = "Lipo Wang and Sen Lin and Zhiyong Tao and Bing Zeng and Xiaowei Hui and Liangshan Shao and Jie Liang",

booktitle = "Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015",

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Zhao, L, Ren, Y & Yan, B 2016, Iterative evolution of feature space in text classification. in L Wang, S Lin, Z Tao, B Zeng, X Hui, L Shao & J Liang (eds), Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015., 7408065, Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1210-1214, 8th International Congress on Image and Signal Processing, CISP 2015, Shenyang, China, 14/10/15. https://doi.org/10.1109/CISP.2015.7408065

Iterative evolution of feature space in text classification. / Zhao, Liutao; Ren, Yitian; Yan, Bo.
Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015. ed. / Lipo Wang; Sen Lin; Zhiyong Tao; Bing Zeng; Xiaowei Hui; Liangshan Shao; Jie Liang. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1210-1214 7408065 (Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015).

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

TY - GEN

T1 - Iterative evolution of feature space in text classification

AU - Zhao, Liutao

AU - Ren, Yitian

AU - Yan, Bo

PY - 2016/2/16

Y1 - 2016/2/16

N2 - Nature language processing is an important part in data mining, which counts a lot in the internet age. Feature extraction effects the accuracy of text classification. This paper proposes a method of iterative feature space evolution to optimize the result. Adjusting the extended dictionary and the stop word list, we optimize the feature space time and again to get a better classifier model. The final result has a higher classification accuracy than the original experiment.

AB - Nature language processing is an important part in data mining, which counts a lot in the internet age. Feature extraction effects the accuracy of text classification. This paper proposes a method of iterative feature space evolution to optimize the result. Adjusting the extended dictionary and the stop word list, we optimize the feature space time and again to get a better classifier model. The final result has a higher classification accuracy than the original experiment.

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KW - feature extraction

KW - feature space

KW - text classification

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DO - 10.1109/CISP.2015.7408065

M3 - Conference contribution

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T3 - Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015

SP - 1210

EP - 1214

BT - Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015

A2 - Wang, Lipo

A2 - Lin, Sen

A2 - Tao, Zhiyong

A2 - Zeng, Bing

A2 - Hui, Xiaowei

A2 - Shao, Liangshan

A2 - Liang, Jie

PB - Institute of Electrical and Electronics Engineers Inc.

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Zhao L, Ren Y, Yan B. Iterative evolution of feature space in text classification. In Wang L, Lin S, Tao Z, Zeng B, Hui X, Shao L, Liang J, editors, Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1210-1214. 7408065. (Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015). doi: 10.1109/CISP.2015.7408065

Iterative evolution of feature space in text classification

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