mlquantify (version 0.2.0)

SORD: Sample ORD Dissimilarity

Description

It quantifies events based on testing scores applying the framework DyS with the Sample ORD Dissimilarity (SORD) proposed by Maletzke et al. (2019).

Usage

SORD(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<U+2019>19, 2019.<doi.org/10.1609/aaai.v33i01.33014552>.

Examples

# NOT RUN {
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"))
SORD(p.score = scores[scores[,3]==1,1], n.score = scores[scores[,3]==2,1],
test = test.scores[,1])
# }