Algorithms for Class Distribution Estimation
c(
person("Andre", "Maletzke", email = "andregustavom@gmail.com", role = c("aut","cre")),
person("Everton", "Cherman", email = "evertoncherman@gmail.com", role = "ctb"),
person("Denis", "dos Reis", email = "denismr@gmail.com", role = "ctb"),
person("Gustavo", "Batista", email = "g.batista@unsw.edu.au", role = "ths"))
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>.
Andre Maletzke [aut, cre],
Everton Cherman [ctb],
Denis dos Reis [ctb],
Gustavo Batista [ths]

Interface to a large number of classification and
regression techniques, including machine-readable parameter
descriptions. Generic resampling, including cross-validation,
bootstrapping and subsampling. Hyperparameter tuning with
modern optimization techniques. Filter and wrapper methods for
feature selection. Extension of basic learners with additional
operations. Nested resampling.
BBmisc (>= 1.3-64), parallelMap, codetools
testthat, snowfall, ada, adabag, cmaes, DiceKriging,
DiceOptim, e1071, earth, FNN, FSelector, gbm, irace, kernlab,
kknn, klaR, mboost, mda, mlbench, nnet, party, penalized, pls,
randomForest, reshape, robustbase, rpart, rsm, RWeka, ROCR

Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also a n experimental extension for surival analysis and cost-sensitive
learning. Generic resampling, including cross-validation, bootstrapping and
subsampling. Hyperparameter tuning with modern optimization techniques.
Filter and wrapper methods for feature selection. Extension of basic
learners with additional operations common in machine learning.
Bernd Bischl <bernd_bischl@gmx.net>,
Michel Lang <lang@statistik.tu-dortmund.de>,
Jakob Richter <code@jakob-r.de>,
Leonard Judt <leonard.judt@tu-dortmund.de>
parallelMap (>= 1.1), codetools, survival, checkmate (>= 1.1)
testthat, ada, adabag, class, cluster, cmaes, CoxBoost, crs,
DiceKriging, DiceOptim, DiscriMiner, e1071, earth, FNN,
FSelector, gbm, GenSA, ggplot2, glmnet, Hmisc, irace, kernlab,
kknn, klaR, LiblineaR, mboost, mda, mlbench, mRMRe, nnet,
party, penalized, pls, pROC, randomForest, randomForestSRC,
reshape2, rrlda, robustbase, rpart, rsm, RWeka, ROCR, stepPlr