MKS: Mixable Kolmogorov Smirnov

In mlquantify: Algorithms for Class Distribution Estimation

Description

It quantifies events based on testing scores, applying the Mixable Kolmogorov Smirnov (MKS) method proposed by Maletzke et al. (2019).

Usage

MKS(p.score, n.score, test)

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.

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"))
MKS(p.score = scores[scores[,3]==1,1], n.score = scores[scores[,3]==2,1],
test = test.scores)

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