Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alaiz-Rodríguez, R., Alegre, E., González-Castro, V., Sánchez, L.: Quantifying the proportion of damaged sperm cells based on image analysis and neural networks. In: SMO’08: Proc. of the 8th conf. on Simulation, modelling and optimization, pp. 383–388 (2008)
Alegre, E., González-Castro, V., Suárez, S., Castejón, M.: Comparison of supervised and unsupervised methods to classify boar acrosomes using texture descriptors. In: Proceedings ELMAR-2009, September 2009, pp. 65–70 (2009)
Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recogn. Lett. 24(9-10), 1513–1521 (2003)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1996)
Chan, Y.S., Ng, H.T.: Estimating class priors in domain adaptation for word sense disambiguation. In: ACL-44: Proc. of the 21st Int. Conf. on Computational Linguistics, pp. 89–96 (2006)
Cieslak, D., Chawla, N.: A framework for monitoring classiffiers performance: When and why failure occurs? Knowl. Inf. Syst. 18(1), 83–108 (2009)
Csiszar, I., Shields, P.: Information Theory and Statistics: A Tutorial (Foundations and Trends in Communications and Information The). Now Publishers Inc. (December 2004)
Forman, G.: Quantifying counts and costs via classifcation. Data Min. Knowl. Disc. 17(2), 164–206 (2008)
González, M., Alegre, E., Alaiz, R., Sánchez, L.: Acrosome integrity classification of boar spermatozoon images using dwt and texture techniques. In: International Conference VipIMAGE 2007. Taylor & Francis, Abington (2007)
Saerens, M., Latinne, P., Decaestecker, C.: Adjusting a classifier for new a priori probabilities: A simple procedure. Neural Comput. 14, 21–41 (2002)
Xue, J.C., Weiss, G.M.: Quantification and semi-supervised classification methods for handling changes in class distribution. In: Proc. of the 15th ACM SIGKDD int. conf. on Knowledge discovery and data mining, pp. 897–906 (2009)
Yang, C., Zhou, J.: Non-stationary data sequence classification using online class priors estimation. Pattern Recogn. 41(8), 8 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
González-Castro, V., Alaiz-Rodríguez, R., Fernández-Robles, L., Guzmán-Martínez, R., Alegre, E. (2010). Estimating Class Proportions in Boar Semen Analysis Using the Hellinger Distance. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-13022-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13021-2
Online ISBN: 978-3-642-13022-9
eBook Packages: Computer ScienceComputer Science (R0)