Algorithms for Class Distribution Estimation
Adjusted Classify and Count
Classify and Count
DyS Framework
Expectation-Maximization Quantification
Estimates true and false positive rates
HDy with Laplace smoothing
Quantification method based on Kuiper's test
Threshold selection method
Mixable Kolmogorov Smirnov
Median Sweep
Threshold selection method. Median Sweep
Probabilistic Adjusted Classify and Count
Probabilistic Classify and Count
Proportion-weighted k-nearest neighbor
Sample Mean Matching
Sample ORD Dissimilarity
Threshold selection method
Threshold selection method
Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. <doi:10.24963/ijcai.2020/366>.
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