Neural networks for pattern recognition
Neural networks for pattern recognition
Robust Classification for Imprecise Environments
Machine Learning
Guide to Neural Computing Applications
Guide to Neural Computing Applications
Classification on Data with Biased Class Distribution
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
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
Pragmatic text mining: minimizing human effort to quantify many issues in call logs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Information theory and statistics: a tutorial
Communications and Information Theory
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Estimating class priors in domain adaptation for word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Quantifying counts and costs via classification
Data Mining and Knowledge Discovery
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
A framework for monitoring classifiers’ performance: when and why failure occurs?
Knowledge and Information Systems
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Quantifying the proportion of damaged sperm cells based on image analysis and neural networks
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
International Journal of Approximate Reasoning
Quantification and semi-supervised classification methods for handling changes in class distribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Information Sciences: an International Journal
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Assessing the impact of changing environments on classifier performance
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Quantification via Probability Estimators
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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
A unifying view on dataset shift in classification
Pattern Recognition
Counting positives accurately despite inaccurate classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
Information Sciences: an International Journal
Vitality assessment of boar sperm using an adaptive LBP based on oriented deviation
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Aggregative quantification for regression
Data Mining and Knowledge Discovery
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Class distribution estimation (quantification) plays an important role in many practical classification problems. Firstly, it is important in order to adapt the classifier to the operational conditions when they differ from those assumed in learning. Additionally, there are some real domains where the quantification task is itself valuable due to the high variability of the class prior probabilities. Our novel quantification approach for two-class problems is based on distributional divergence measures. The mismatch between the test data distribution and validation distributions generated in a fully controlled way is measured by the Hellinger distance in order to estimate the prior probability that minimizes this divergence. Experimental results on several binary classification problems show the benefits of this approach when compared to such approaches as counting the predicted class labels and other methods based on the classifier confusion matrix or on posterior probability estimations. We also illustrate these techniques as well as their robustness against the base classifier performance (a neural network) with a boar semen quality control setting. Empirical results show that the quantification can be conducted with a mean absolute error lower than 0.008, which seems very promising in this field.