TY - GEN
T1 - System evaluation of construction methods for multi-class problems using binary classifiers
AU - Hirasawa, Shigeichi
AU - Kumoi, Gendo
AU - Kobayashi, Manabu
AU - Goto, Masayuki
AU - Inazumi, Hiroshige
N1 - Funding Information:
Acknowledgment. One of the authors S. H. would like to thank Professor Shin’ ichi Oishi of Waseda University for giving a chance to study this work. The research leading to this paper was partially supported by MEXT Kakenhi under Grant-in Aids for Scientific Research (B) No. 26282090 and (C) No. 16K00491.
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Construction methods for multi-valued classification (multi-class) systems using binary classifiers are discussed and evaluated by a trade-off model for system evaluation based on rate-distortion theory. Suppose the multi-class systems consisted of M(≥3) categories and N(≥M-1) binary classifiers, then they can be represented by a matrix W, where the matrix W is given by a table of M code words with length N, called a code word table. For a document classification task, the relationship between the probability of classification error Pe and the number of binary classifiers N for given M is investigated, and we show that our constructed systems satisfy desirable properties such as “Flexible”, and “Elastic”. In particular, modified Reed Muller codes perform well: they are shown to be “Effective elastic”. As a second application we consider a hand-written character recognition task, and we show that the desirable properties are also satisfied.
AB - Construction methods for multi-valued classification (multi-class) systems using binary classifiers are discussed and evaluated by a trade-off model for system evaluation based on rate-distortion theory. Suppose the multi-class systems consisted of M(≥3) categories and N(≥M-1) binary classifiers, then they can be represented by a matrix W, where the matrix W is given by a table of M code words with length N, called a code word table. For a document classification task, the relationship between the probability of classification error Pe and the number of binary classifiers N for given M is investigated, and we show that our constructed systems satisfy desirable properties such as “Flexible”, and “Elastic”. In particular, modified Reed Muller codes perform well: they are shown to be “Effective elastic”. As a second application we consider a hand-written character recognition task, and we show that the desirable properties are also satisfied.
KW - Binary classifier
KW - ECOC
KW - Error correcting codes
KW - Exhaustive code
KW - Multi-valued classification
KW - Trade-off model
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U2 - 10.1007/978-3-319-77712-2_86
DO - 10.1007/978-3-319-77712-2_86
M3 - Conference contribution
AN - SCOPUS:85045309941
SN - 9783319777115
T3 - Advances in Intelligent Systems and Computing
SP - 909
EP - 919
BT - Trends and Advances in Information Systems and Technologies
A2 - Reis, Luis Paulo
A2 - Rocha, Alvaro
A2 - Costanzo, Sandra
A2 - Adeli, Hojjat
PB - Springer Verlag
T2 - 6th World Conference on Information Systems and Technologies, WorldCIST 2018
Y2 - 27 March 2018 through 29 March 2018
ER -