TY - GEN
T1 - A new disagreement measure for characterization of classification problems
AU - Ledeneva, Yulia
AU - García-Hernández, René Arnulfo
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Robert P.W. Duin, Elzbieta Pekalska and David M.J. Tax proposed the characterization of classification problems by classifier disagreement. They showed that it is possible to use a standard set of supervised classification problems for constructing a rule that allows deciding about the similarity of new problems to the existing ones. The classifier disagreement could be used to group classification problems in a way which could help to select the appropriate tools for solving new problems. Duin et al proposed a dissimilarity measure between two problems taking into account only the full disagreement matrices. They used a measure of the disagreement based on the coincidence of the classifier output however the correctness was not considered. In this work, we propose a new measure of disagreement which takes into account the correctness of classification result. To calculate the disagreement each object is analyzed to verify if it was classified correctly or incorrectly by the classifiers. We use this new disagreement measure to calculate the dissimilarity between two problems. Some experiments were done and the results were compared against Duin’s et al results.
AB - Robert P.W. Duin, Elzbieta Pekalska and David M.J. Tax proposed the characterization of classification problems by classifier disagreement. They showed that it is possible to use a standard set of supervised classification problems for constructing a rule that allows deciding about the similarity of new problems to the existing ones. The classifier disagreement could be used to group classification problems in a way which could help to select the appropriate tools for solving new problems. Duin et al proposed a dissimilarity measure between two problems taking into account only the full disagreement matrices. They used a measure of the disagreement based on the coincidence of the classifier output however the correctness was not considered. In this work, we propose a new measure of disagreement which takes into account the correctness of classification result. To calculate the disagreement each object is analyzed to verify if it was classified correctly or incorrectly by the classifiers. We use this new disagreement measure to calculate the dissimilarity between two problems. Some experiments were done and the results were compared against Duin’s et al results.
UR - http://www.scopus.com/inward/record.url?scp=84947584682&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-20469-7_16
DO - 10.1007/978-3-319-20469-7_16
M3 - Contribución a la conferencia
SN - 9783319204680
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 144
BT - Advances in Swarm and Computational Intelligence - 6th International Conference, ICSI 2015 held in conjunction with the 2nd BRICS Congress, CCI 2015, Proceedings
A2 - Engelbrecht, Andries
A2 - Buarque, Fernando
A2 - Gelbukh, Alexander
A2 - Shi, Yuhui
A2 - Das, Swagatam
A2 - Tan, Ying
PB - Springer Verlag
T2 - 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015
Y2 - 25 June 2015 through 28 June 2015
ER -