Computes an ensemble score using the maximum score of each observation

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>.

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
#>   [1] 0.4824121 0.1843885 0.4723618 0.4245960 0.6030151 0.4371859 0.7788945
#>   [8] 0.3216080 0.6180905 0.8418571 0.2847566 0.4020101 0.3366834 0.8944724
#>  [15] 0.3618090 0.2663317 0.5577889 0.9849246 0.6482412 0.2321253 0.1805080
#>  [22] 0.4765659 0.6582915 0.2335631 0.7638191 0.3161958 0.4525118 0.4683738
#>  [29] 0.4271357 0.8291457 0.7939698 0.8705872 0.9145729 0.4550387 0.4773869
#>  [36] 0.7386935 0.4435878 0.5181921 0.7267460 0.9748744 0.2534985 0.5979899
#>  [43] 0.7135678 0.8592965 0.6934673 0.5728643 0.7969985 0.2428372 0.4245872
#>  [50] 0.4502043 0.5829146 0.5513504 0.6331658 0.4874372 0.6633166 0.6381910
#>  [57] 0.8894472 0.2512563 0.3925861 0.5778894 0.5445529 0.5326633 0.9556932
#>  [64] 0.2261322 0.7839196 0.4924623 0.4860508 0.3467337 0.3376929 0.9497487
#>  [71] 0.3015075 0.9798995 0.9396985 0.6532663 0.5678392 0.5276382 0.9142872
#>  [78] 0.5427136 0.8743719 0.7955290 0.2261307 0.2976067 0.6387257 0.4442982
#>  [85] 0.8813118 0.4301861 0.2436648 0.4522613 0.7125962 0.2632777 0.8241206
#>  [92] 0.5175879 0.6432161 0.3115578 0.7587940 0.6281407 0.9246231 0.7085427
#>  [99] 1.0000000 0.2484718 0.2461681 0.3958406 0.2822462 0.6683417 0.8341709
#> [106] 0.4707032 0.8793970 0.4623116 0.3718593 0.8492462 0.7487437 0.2778010
#> [113] 0.9547739 0.5485175 0.6834171 0.3165829 0.3954625 0.5929648 0.6231156
#> [120] 0.5125628 0.8693467 0.2459346 0.2423738 0.4065936 0.4120603 0.2357256
#> [127] 0.7537688 0.3567839 0.2331899 0.5630866 0.6884422 0.8040201 0.7437186
#> [134] 0.4673367 0.9095477 0.7889447 0.8844221 0.4109166 0.5527638 0.4321608
#> [141] 0.2964824 0.9346734 0.8391960 0.2769581 0.3098454 0.5376884 0.2893679
#> [148] 0.6080402 0.6984925 0.8542714 0.5075377 0.7236181 0.5445529 0.3969849
#> [155] 0.3919598 0.7384844 0.4221106 0.9418254 0.9648241 0.8994975 0.6783920
#> [162] 0.9447236 0.8643216 0.9949749 0.3266332 0.8442211 0.4422111 0.2881917
#> [169] 0.3768844 0.5249996 0.7738693 0.1875641 0.5686342 0.7286432 0.8190955
#> [176] 0.1818564 0.5477387 0.4152925 0.6733668 0.5226131 0.2864322 0.2875074
#> [183] 0.9421850 0.9698492 0.9597990 0.1914317 0.7989950 0.7185930 0.7336683
#> [190] 0.6130653 0.4974874 0.9045226 0.9296482 0.8090452 0.5879397 0.2814070
#> [197] 0.7688442 0.5025126 0.9899497 1.0000000