Presentation | 2018-12-07 Feature Selection for Document Classification focused on Support Vector Kota Sakasegawa, Sachio Hirokawa, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Feature selection is a well-known approach for improving the prediction performance of document classifcation, where crucial words are selected and used in vectorization of the documents. This paper proposes an improvement of feature selection of [Sakai & Hirokawa 2012] by considering the occurences of words in ths support vectors. We conducted the evaluation of the proposed method on reuter dataset and confirmed that the proposed method yields allmost the same performance with a small number of feature words. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | document classifcation / machine learning / feature selection / SVM |
Paper # | AI2018-25 |
Date of Issue | 2018-11-30 (AI) |
Conference Information | |
Committee | AI |
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Conference Date | 2018/12/7(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
Chair | Tsunenori Mine(Kyushu Univ.) |
Vice Chair | Daisuke Katagami(Tokyo Polytechnic Univ.) / Naoki Fukuta(Shizuoka Univ.) |
Secretary | Daisuke Katagami(Ritsumeikan Univ.) / Naoki Fukuta(Univ. of Electro-Comm.) |
Assistant | Yuko Sakurai(AIST) |
Paper Information | |
Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Feature Selection for Document Classification focused on Support Vector |
Sub Title (in English) | |
Keyword(1) | document classifcation |
Keyword(2) | machine learning |
Keyword(3) | feature selection |
Keyword(4) | SVM |
1st Author's Name | Kota Sakasegawa |
1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
2nd Author's Name | Sachio Hirokawa |
2nd Author's Affiliation | Kyushu University(Kyushu Univ.) |
Date | 2018-12-07 |
Paper # | AI2018-25 |
Volume (vol) | vol.118 |
Number (no) | AI-350 |
Page | pp.pp.1-4(AI), |
#Pages | 4 |
Date of Issue | 2018-11-30 (AI) |