icwa_ensemble: Computes an ensemble score using inverse cluster weighted...

In sevvandi/outlierensembles: A Collection of Outlier Ensemble Algorithms

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)
if (requireNamespace("dbscan", quietly = TRUE)) {
X <- data.frame(x1 = rnorm(200), x2 = rnorm(200))
X[199, ] <- c(4, 4)
X[200, ] <- c(-3, 5)
# Using different parameters of lof for anomaly detection
y1 <- dbscan::lof(X, minPts = 10)
y2 <- dbscan::lof(X, minPts = 20)
knnobj <- dbscan::kNN(X, k = 20)
# Using different KNN distances as anomaly scores
y3 <- knnobj$dist[ ,10]
y4 <- knnobj$dist[ ,20]
# Dense points are less anomalous. Hence 1 - pointdensity is used.
y5 <- 1 - dbscan::pointdensity(X, eps = 0.8, type = "gaussian")
y6 <- 1 - dbscan::pointdensity(X, eps = 0.5, type = "gaussian")
Y <- cbind.data.frame(y1, y2, y3, y4, y5, y6)
ens <- icwa_ensemble(Y)
ens
}

sevvandi/outlierensembles documentation built on June 1, 2025, 4:58 p.m.