Package 'outlierensembles'

Uses the mean as the ensemble score

Description

This function uses the mean as the ensemble score.

Usage

average_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.

Value

The ensemble scores.

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 <- average_ensemble(Y)
ens

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Computes an ensemble score using the greedy algorithm proposed by Schubert et al (2012)

Description

This function computes an ensemble score using the greedy algorithm in the paper titled Evaluation of Outlier Rankings and Outlier Scores by Schubert et al (2012) <doi:10.1137/1.9781611972825.90>. The greedy ensemble is detailed in Section 4.3.

Usage

greedy_ensemble(X, kk = 5)

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.
kk	The number of estimated outliers.

Value

A list with the components:

scores	The ensemble scores.
methods	The methods that are chosen for the ensemble.
chosen	The chosen subset of original anomaly scores.

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 <- greedy_ensemble(Y, kk=5)
ens$scores

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Computes an ensemble score using inverse cluster weighted averaging method by Chiang et al (2017)

Description

This function computes an ensemble score using inverse cluster weighted averaging in the paper titled A Study on Anomaly Detection Ensembles by Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002>. The ensemble is detailed in Algorithm 2.

Usage

icwa_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.

Value

The ensemble scores.

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 <- icwa_ensemble(Y)
ens

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Computes an ensemble score using Item Response Theory

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

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Computes an ensemble score using the maximum score of each observation

Description

This function computes an ensemble score using the maximum score for each observation as detailed in Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>.

Usage

max_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.

Value

The ensemble scores.

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 <- max_ensemble(Y)
ens

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Computes an ensemble score by aggregating values above the mean

Description

This function computes an ensemble score by aggregating values above the mean as detailed in Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>.

Usage

threshold_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.

Value

The ensemble scores.

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 <- threshold_ensemble(Y)
ens

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