TY - GEN
T1 - Estimating class proportions in boar semen analysis using the Hellinger distance
AU - González-Castro, Víctor
AU - Alaiz-Rodríguez, Rocío
AU - Fernández-Robles, Laura
AU - Guzmán-Martínez, R.
AU - Alegre, Enrique
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Advances in image analysis make possible the automatic semen analysis in the veterinary practice. The proportion of sperm cells with damaged/intact acrosome, a major aspect in this assessment, depends strongly on several factors, including animal diversity and manipulation/conservation conditions. For this reason, the class proportions have to be quantified for every future (test) semen sample. In this work, we evaluate quantification approaches based on the confusion matrix, the posterior probability estimates and a novel proposal based on the Hellinger distance. Our information theoretic-based approach to estimate the class proportions measures the similarity between several artificially generated calibration distributions and the test one at different stages: the data distributions and the classifier output distributions. Experimental results show that quantification can be conducted with a Mean Absolute Error below 0.02, what seems promising in this field.
AB - Advances in image analysis make possible the automatic semen analysis in the veterinary practice. The proportion of sperm cells with damaged/intact acrosome, a major aspect in this assessment, depends strongly on several factors, including animal diversity and manipulation/conservation conditions. For this reason, the class proportions have to be quantified for every future (test) semen sample. In this work, we evaluate quantification approaches based on the confusion matrix, the posterior probability estimates and a novel proposal based on the Hellinger distance. Our information theoretic-based approach to estimate the class proportions measures the similarity between several artificially generated calibration distributions and the test one at different stages: the data distributions and the classifier output distributions. Experimental results show that quantification can be conducted with a Mean Absolute Error below 0.02, what seems promising in this field.
UR - http://www.scopus.com/inward/record.url?scp=79551511496&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13022-9_29
DO - 10.1007/978-3-642-13022-9_29
M3 - Conference contribution
AN - SCOPUS:79551511496
SN - 3642130216
SN - 9783642130212
VL - 6096 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 284
EP - 293
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010
Y2 - 1 June 2010 through 4 June 2010
ER -