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
Computational Vision and Medical Image Processing: VipIMAGE 2007
Computational Vision and Medical Image Processing: VipIMAGE 2007
Counting positives accurately despite inaccurate classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
Aggregative quantification for regression
Data Mining and Knowledge Discovery
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Classifying damaged-intact cells in a semen sample presents the peculiarity that the test class distribution is unknown. This paper studies under which design conditions the misclassification rate is minimum for the uncertainty region of interest (ratio of damaged cells lower than 20%) and (b) deals with quantifying the proportion of damaged/intact sperm cells in a given sample based on computer vision and supervised learning. We have applied a discrete wavelet transform to the spermatozoa head images and computed the mean and standard deviation (WSF) and four Haralick descriptors (WCF). Using a backpropagation neural network, the error rate averaged over distributions in the region of interest is 4.85% with WCF. The assessment of several quantification methods shows the conditions under which the Adjusted Count method leads to an overall mean absolute error of 3.2 and the Classify & Count method yields 2.4, both with WCF features. Deviations of this order are considered reasonable for this field.