DyS: DyS Framework

In mlquantify: Algorithms for Class Distribution Estimation

Description

DyS is a framework for quantification data based on mixture models method. It quantifies events based on testing scores, applying the DyS framework proposed by Maletzke et al. (2019). It also works with several similarity functions.

Usage

DyS(p.score, n.score, test, measure="topsoe", bins=seq(2,20,2), err=1e-5)

Arguments

p.score	a numeric vector of positive scores estimated either from a validation set or from a cross-validation method.
n.score	a numeric vector of negative scores estimated either from a validation set or from a cross-validation method.
test	a numeric vector containing the score estimated for the positive class from each test set instance.
measure	measure used to compare the mixture histogram against the histogram obtained from the test set. Several functions can be used (Default: "topsoe", "euclidean", "jensen_difference", "prob_symm", "taneja", "ord").
bins	a numeric vector of number of bins used to construct the histogram for representing the score distribution. (default: seq(2,20,2)).
err	a numeric value defining the accepted error for the ternary search (default: 1e5).

Value

A numeric vector containing the class distribution estimated from the test set.

References

Maletzke, A., Reis, D., Cherman, E., & Batista, G. (2019). DyS: a Framework for Mixture Models in Quantification. in Proceedings of the The Thirty-Third AAAI Conference on Artificial Intelligence, ser. AAAI’19, 2019. <doi.org/10.1609/aaai.v33i01.33014552>.

Examples

library(randomForest)
library(caret)
cv <- createFolds(aeAegypti$class, 3)
tr <- aeAegypti[cv$Fold1,]
validation <- aeAegypti[cv$Fold2,]
ts <- aeAegypti[cv$Fold3,]

# -- Getting a sample from ts with 80 positive and 20 negative instances --
ts_sample <- rbind(ts[sample(which(ts$class==1),80),],
                   ts[sample(which(ts$class==2),20),])
scorer <- randomForest(class~., data=tr, ntree=500)
scores <- cbind(predict(scorer, validation, type = c("prob")), validation$class)
test.scores <- predict(scorer, ts_sample, type = c("prob"))
DyS(p.score = scores[scores[,3]==1,1], n.score = scores[scores[,3]==2,1],
test = test.scores[,1])

mlquantify documentation built on Jan. 20, 2022, 5:07 p.m.