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

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)
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 <- greedy_ensemble(Y, kk=5)
ens$scores
}
#>   [1] 0.7749993012 0.4321599887 0.5845229698 0.1135792510 0.0405051381
#>   [6] 0.6680514135 0.2223657022 0.4965478184 0.6010439417 0.0667362881
#>  [11] 0.4265269819 0.0779196815 0.4636297538 0.0654657170 0.3314708968
#>  [16] 0.8692956823 0.1225212510 0.7959482599 0.4853286790 0.4388285275
#>  [21] 0.3999652812 0.1398132907 0.4893076857 0.2774196307 0.1607170913
#>  [26] 0.6260662033 0.3238360879 0.1368795405 0.4687222476 0.5902465371
#>  [31] 0.7472331322 0.0289949871 0.2763927211 0.3514772043 0.2796187047
#>  [36] 0.5174632748 0.1043156542 0.0690797097 0.0693847552 0.2023193364
#>  [41] 0.3003037742 0.0754717936 0.6650247406 0.8771846937 0.4089033558
#>  [46] 0.7434417932 0.0556889490 0.4715188911 0.2876897553 0.0891472976
#>  [51] 0.0482615925 0.0764253595 0.0228636810 0.4954388633 0.5575243077
#>  [56] 0.5598394378 0.6615655707 0.2605402145 0.0023250595 0.5311351962
#>  [61] 0.1093243562 0.1438981704 0.0384331435 0.5436525712 0.8344086221
#>  [66] 0.5819889776 0.0670119603 0.6231586237 0.2862226920 0.7758467085
#>  [71] 0.2608609575 0.8740569542 0.4412006702 0.5394834618 0.3077856419
#>  [76] 0.3453649058 0.0401303294 0.4039356114 0.0890631604 0.0059267895
#>  [81] 0.5814584499 0.2428726486 0.0955122967 0.1454101380 0.0229667107
#>  [86] 0.0511675511 0.3749989246 0.6395189270 0.0445290813 0.3939189192
#>  [91] 0.5930542469 0.4206802773 0.3933743157 0.2106372511 0.8696570321
#>  [96] 0.1201878422 0.9283598428 0.7671617026 0.0183327988 0.5306310317
#> [101] 0.2824332689 0.1642667799 0.2422186485 0.3097589201 0.3169699829
#> [106] 0.0461064947 0.7766154274 0.6176084867 0.4046182265 0.8279041183
#> [111] 0.4517522100 0.3156802987 0.8595566936 0.0901709037 0.1117653912
#> [116] 0.2096395226 0.0005528566 0.4660106207 0.6345939486 0.4329329206
#> [121] 0.0328477093 0.4994269461 0.4665288002 0.1588345039 0.7216827875
#> [126] 0.2471166052 0.0158499628 0.1473281099 0.5403142381 0.0000000000
#> [131] 0.6868181249 0.4002920627 0.1321704736 0.5274097336 0.7717270374
#> [136] 0.8503815329 0.7491891435 0.1803031382 0.7912951009 0.5194736080
#> [141] 0.3004627488 0.2771495570 0.7817190081 0.5475957336 0.5909050078
#> [146] 0.6510383094 0.5221416367 0.2237049239 0.8458370987 0.4478975499
#> [151] 0.4755519959 0.7861689222 0.0746500941 0.2997910801 0.2083957662
#> [156] 0.0672074803 0.1819272722 0.0463626981 0.9106807305 0.8670441451
#> [161] 0.3309732831 0.3828086662 0.4295528820 0.9770364454 0.2378049410
#> [166] 0.0334033164 0.1997473504 0.2926042209 0.3285991278 0.0910525476
#> [171] 0.8339747516 0.5759822127 0.0487837887 0.8392857135 0.2321542420
#> [176] 0.4164914937 0.2528132316 0.0478132449 0.0650321757 0.6570731688
#> [181] 0.3258195272 0.4532622411 0.0535404725 0.4561143945 0.0965924621
#> [186] 0.4617203609 0.4260049940 0.3226702035 0.2179518285 0.1190478348
#> [191] 0.1179842233 0.7809801904 0.2535155282 0.3919957821 0.5134769196
#> [196] 0.8771084141 0.2589282624 0.5042717860 1.0000000000 1.0000000000