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Data-Adaptive ECOC for Multicategory Classification
석경하 한국데이터정보과학회 2008 한국데이터정보과학회지 Vol.19 No.1
Error Correcting Output Codes(ECOC) can improve generalization performance when applied to multicategory classification problem. In this study we propose a new criterion to select hyperparameters included in ECOC scheme. Instead of margins of a data we propose to use the probability of misclassification error since it makes the criterion simple. Using this we obtain an upper bound of leave-one-out error of OVA(one vs all) method. Our experiments from real and synthetic data indicate that the bound leads to good estimates of parameters.
Comparison of Various Criteria for Designing ECOC
석경하,이승철,전갑동 한국데이터정보과학회 2006 한국데이터정보과학회지 Vol.17 No.2
Error Correcting Output Coding(ECOC) is used to solve multi-class problem. It is known that it improves the classification accuracy. In this paper, we compared various criteria to design code matrix while encoding. In addition. we prorpose an ensemble which uses the ability of each classifier while decoding. We investigate the justification of the proposed method through real data and synthetic data.
Hyperparameter Selection for APC-ECOC
석경하 한국데이터정보과학회 2008 한국데이터정보과학회지 Vol.19 No.4
The main object of this paper is to develop a leave-one-out(LOO) bound of all pairwise comparison error correcting output codes (APC-ECOC). To avoid using classifiers whose corresponding target values are 0 in APC-ECOC and requiring pilot estimates we developed a bound based on mean misclassification probability(MMP). It can be used to tune kernel hyperparameters. Our empirical experiment using kernel mean squared estimate(KMSE) as the binary classifier indicates that the bound leads to good estimates of kernel hyperparameters.
Comparison of Various Criteria for Designing ECOC
Seok, Kyeong-Ha,Lee, Seung-Chul,Jeon, Gab-Dong 한국데이터정보과학회 2006 한국데이터정보과학회지 Vol.17 No.2
Error Correcting Output Coding(ECOC) is used to solve multi-class problem. It is known that it improves the classification accuracy. In this paper, we compared various criteria to design code matrix while encoding. In addition. we prorpose an ensemble which uses the ability of each classifier while decoding. We investigate the justification of the proposed method through real data and synthetic data.
Data-Adaptive ECOC for Multicategory Classification
Seok, Kyung-Ha 한국데이터정보과학회 2008 한국데이터정보과학회지 Vol.19 No.1
Error Correcting Output Codes (ECOC) can improve generalization performance when applied to multicategory classification problem. In this study we propose a new criterion to select hyperparameters included in ECOC scheme. Instead of margins of a data we propose to use the probability of misclassification error since it makes the criterion simple. Using this we obtain an upper bound of leave-one-out error of OVA(one vs all) method. Our experiments from real and synthetic data indicate that the bound leads to good estimates of parameters.
Hyperparameter Selection for APC-ECOC
Seok, Kyung-Ha 한국데이터정보과학회 2008 한국데이터정보과학회지 Vol.19 No.4
The main object of this paper is to develop a leave-one-out(LOO) bound of all pairwise comparison error correcting output codes (APC-ECOC). To avoid using classifiers whose corresponding target values are 0 in APC-ECOC and requiring pilot estimates we developed a bound based on mean misclassification probability(MMP). It can be used to tune kernel hyperparameters. Our empirical experiment using kernel mean squared estimate(KMSE) as the binary classifier indicates that the bound leads to good estimates of kernel hyperparameters.