TY - GEN
T1 - Proposal for a unified methodology for evaluating supervised and non-supervised classification algorithms
AU - Godoy-Calderón, Salvador
AU - Martínez-Trinidad, J. Fco
AU - Cortés, Manuel Lazo
PY - 2006
Y1 - 2006
N2 - There is presently no unified methodology that allows the evaluation of supervised and non-supervised classification algorithms. Supervised problems are evaluated through Quality Functions that require a previously known solution for the problem, while non-supervised problems are evaluated through several Structural Indexes that do not evaluate the classification algorithm by using the same pattern similarity criteria embedded in the classification algorithm. In both cases, a lot of useful information remains hidden or is not considered by the evaluation method, such as the quality of the supervision sample or the structural change generated by the classification algorithm on the sample. This paper proposes a unified methodology to evaluate classification problems of both kinds, that offers the possibility of making comparative evaluations and yields a larger amount of information to the evaluator about the quality of the initial sample, when it exists, and regarding the change produced by the classification algorithm.
AB - There is presently no unified methodology that allows the evaluation of supervised and non-supervised classification algorithms. Supervised problems are evaluated through Quality Functions that require a previously known solution for the problem, while non-supervised problems are evaluated through several Structural Indexes that do not evaluate the classification algorithm by using the same pattern similarity criteria embedded in the classification algorithm. In both cases, a lot of useful information remains hidden or is not considered by the evaluation method, such as the quality of the supervision sample or the structural change generated by the classification algorithm on the sample. This paper proposes a unified methodology to evaluate classification problems of both kinds, that offers the possibility of making comparative evaluations and yields a larger amount of information to the evaluator about the quality of the initial sample, when it exists, and regarding the change produced by the classification algorithm.
UR - http://www.scopus.com/inward/record.url?scp=33845203188&partnerID=8YFLogxK
U2 - 10.1007/11892755_70
DO - 10.1007/11892755_70
M3 - Contribución a la conferencia
AN - SCOPUS:33845203188
SN - 3540465561
SN - 9783540465560
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 674
EP - 685
BT - Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings
PB - Springer Verlag
T2 - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006
Y2 - 14 November 2006 through 17 November 2006
ER -