irt_ensemble: Computes an ensemble score using Item Response Theory

In outlierensembles: A Collection of Outlier Ensemble Algorithms

Description

This function computes an ensemble score using Item Response Theory (IRT). This was proposed as an ensemble method for anomaly/outlier detection in Kandanaarachchi (2021) <doi:10.13140/RG.2.2.18355.96801>.

Usage

irt_ensemble(X)

Arguments

X	The input data containing the outlier scores in a dataframe, matrix or tibble format. Rows contain observations and columns contain outlier detection methods.

Details

For outlier detection, higher ensemble scores indicate higher levels of anomalousness. This ensemble uses IRT's latent trait to uncover the hidden ground truth, which is used as the ensemble score. It uses the R packages airt and EstCRM to fit the IRT models. It can also be used for other ensembling tasks.

Value

A list with the components:

scores	The ensemble scores.
model	The IRT model.

Examples

set.seed(123)
X <- data.frame(x1 = rnorm(200), x2 = rnorm(200))
X[199, ] <- c(4, 4)
X[200, ] <- c(-3, 5)
y1 <- DDoutlier::KNN_AGG(X)
y2 <- DDoutlier::LOF(X)
y3 <- DDoutlier::COF(X)
y4 <- DDoutlier::INFLO(X)
y5 <- DDoutlier::KDEOS(X)
y6 <- DDoutlier::LDF(X)
y7 <- DDoutlier::LDOF(X)
Y <- cbind.data.frame(y1, y2, y3, y4, y5, y6, y7)
ens <- irt_ensemble(Y)
ens$scores

outlierensembles documentation built on June 15, 2021, 9:06 a.m.