TY - GEN
T1 - Decoding of ternary error correcting output codes
AU - Escalera, Sergio
AU - Pujol, Oriol
AU - Radeva, Petia
PY - 2006
Y1 - 2006
N2 - Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. Lately, the ECOC framework was extended from the binary to the ternary case to allow classes to be ignored by a certain classifier, allowing in this way to increase the number of possible dichotomies to be selected. Nevertheless, the effect of the zero symbol by which dichotomies exclude certain classes from consideration has not been previously enough considered in the definition of the decoding strategies, In this paper, we show that by a special treatment procedure of zeros, and adjusting the weights at the rest of coded positions, the accuracy of the system can be increased. Besides, we extend the main state-of-art decoding strategies from the binary to the ternary case, and we propose two novel approaches: Laplacian and Pessimistic Beta Density Probability approaches. Tests on UCI database repository (with different sparse matrices containing different percentages of zero symbol) show that the ternary decoding techniques proposed outperform the standard decoding strategies.
AB - Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. Lately, the ECOC framework was extended from the binary to the ternary case to allow classes to be ignored by a certain classifier, allowing in this way to increase the number of possible dichotomies to be selected. Nevertheless, the effect of the zero symbol by which dichotomies exclude certain classes from consideration has not been previously enough considered in the definition of the decoding strategies, In this paper, we show that by a special treatment procedure of zeros, and adjusting the weights at the rest of coded positions, the accuracy of the system can be increased. Besides, we extend the main state-of-art decoding strategies from the binary to the ternary case, and we propose two novel approaches: Laplacian and Pessimistic Beta Density Probability approaches. Tests on UCI database repository (with different sparse matrices containing different percentages of zero symbol) show that the ternary decoding techniques proposed outperform the standard decoding strategies.
UR - http://www.scopus.com/inward/record.url?scp=33845225201&partnerID=8YFLogxK
U2 - 10.1007/11892755_78
DO - 10.1007/11892755_78
M3 - Article in proceeding
AN - SCOPUS:33845225201
SN - 3540465561
SN - 9783540465560
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 753
EP - 763
BT - Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings
PB - Springer
T2 - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006
Y2 - 14 November 2006 through 17 November 2006
ER -