Performance Evaluation of ECOC Considering Estimated Probability of Binary Classifiers

Gendo Kumoi*, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

*この研究の対応する著者

研究成果: Conference contribution

抄録

Error-Correcting Output Coding (ECOC) is a method for constructing a multi-valued classifier using a combination of the given binary classifiers. ECOC is said to be able to estimate the correct category by other binary classifiers even if the output of some binary classifiers is incorrect based on the framework of the coding theory. Although it is experimentally known that this method performs well on real data, a theoretical analysis of the classification accuracy for ECOC has yet to be conducted. In this study, we evaluate the superiority of a code word table in showing the combinations of binary classifiers of ECOC that have been experimentally demonstrated. In other words, we analytically evaluate how the estimation of the categories is influenced by the estimated posterior probability, which is the output of the binary classifier, as well as by the structure of constructing the code word table.

本文言語English
ホスト出版物のタイトルInformation Systems and Technologies - WorldCIST 2022
編集者Alvaro Rocha, Hojjat Adeli, Gintautas Dzemyda, Fernando Moreira
出版社Springer Science and Business Media Deutschland GmbH
ページ379-389
ページ数11
ISBN(印刷版)9783031048180
DOI
出版ステータスPublished - 2022
イベント10th World Conference on Information Systems and Technologies, WorldCIST 2022 - Budva, Montenegro
継続期間: 2022 4月 122022 4月 14

出版物シリーズ

名前Lecture Notes in Networks and Systems
469 LNNS
ISSN(印刷版)2367-3370
ISSN(電子版)2367-3389

Conference

Conference10th World Conference on Information Systems and Technologies, WorldCIST 2022
国/地域Montenegro
CityBudva
Period22/4/1222/4/14

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 信号処理
  • コンピュータ ネットワークおよび通信

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