Performance Evaluation of ECOC Considering Estimated Probability of Binary Classifiers

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationInformation Systems and Technologies - WorldCIST 2022
EditorsAlvaro Rocha, Hojjat Adeli, Gintautas Dzemyda, Fernando Moreira
PublisherSpringer Science and Business Media Deutschland GmbH
Pages379-389
Number of pages11
ISBN (Print)9783031048180
DOIs
Publication statusPublished - 2022
Event10th World Conference on Information Systems and Technologies, WorldCIST 2022 - Budva, Montenegro
Duration: 2022 Apr 122022 Apr 14

Publication series

NameLecture Notes in Networks and Systems
Volume469 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference10th World Conference on Information Systems and Technologies, WorldCIST 2022
Country/TerritoryMontenegro
CityBudva
Period22/4/1222/4/14

Keywords

  • Error-Correcting Output Coding
  • Estimated posterior probabilities
  • Multi-valued classification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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