Neural networks for pattern recognition
Neural networks for pattern recognition
Texture classification using wavelet transform
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Quantifying trends accurately despite classifier error and class imbalance
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Counting positives accurately despite inaccurate classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
Estimating class proportions in boar semen analysis using the hellinger distance
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images
Computer Methods and Programs in Biomedicine
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
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Insemination techniques in the veterinary field demand more objective methods to control the quality of sperm samples. In particular, different factors may damage a number of sperm cells that is difficult to predict in advance. This paper addresses the problem of quantifying the proportion of damaged/intact sperm cells in a given sample based on computer vision techniques and supervised learning. Unlike common supervised classification approaches, neither the individual example classification is important nor the class distribution assumed in learning can be considered stationary. To deal with this, an estimation process based on Posterior Probability estimates (PP), and known to increase the classifier accuracy under changes in class distributions, is assessed here for quantification purposes. It is compared with two approaches based on the classifier confusion matrix (Adjusted Count and Median Sweep) and the naive approach based on classifying and counting. Experimental results with boar sperm samples and back propagation neural networks show that the PP based quantification outperforms any of the previously considered approaches in terms of the Mean Absolute Error, Kullback Leibler divergence and Mean Relative Error. Moreover, in spite of an imperfect classification, the PP approach guarantees a uniform Mean Absolute Error (around 3%) for whatever combination of training and test class distributions, what is very promising in this practical field.