stab.fs {genefu} | R Documentation |
This function computes several indexes to quantify feature selection stability. This is usually estimated through perturbation of the original dataset by generating multiple sets of selected features.
stab.fs(fsets, N, method = c("kuncheva", "davis"), ...)
fsets |
list of sets of selected features, each set of selected features may have different size |
N |
total number of features on which feature selection is performed |
method |
stability index (see details section) |
... |
additional parameters passed to stability index ( |
Stability indices may use different parameters. In this version only the Davis index requires an additional parameter that is penalty
, a numeric value used as penalty term.
Kuncheva index (kuncheva
) lays in [-1, 1], An index of -1 means no intersection between sets of selected features, +1 means that all the same features are always selected and 0 is the expected stability of a random feature selection.
Davis index (davis
) lays in [0,1], With a pnalty term equal to 0, an index of 0 means no intersection between sets of selected features and +1 means that all the same features are always selected. A penalty of 1 is usually used so that a feature selection performed with no or all features has a Davis stability index equals to 0. None estimate of the expected Davis stability index of a random feature selection was published.
A numeric that is the stability index
Benjamin Haibe-Kains
Davis CA, Gerick F, Hintermair V, Friedel CC, Fundel K, Kuffner R, Zimmer R (2006) "Reliable gene signatures for microarray classification: assessment of stability and performance", Bioinformatics, 22(19):356-2363.
Kuncheva LI (2007) "A stability index for feature selection", AIAP'07: Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference, pages 390–395.
set.seed(54321) ## 100 random selection of 50 features from a set of 10,000 features fsets <- lapply(as.list(1:100), function(x, size=50, N=10000) { return(sample(1:N, size, replace=FALSE))} ) names(fsets) <- paste("fsel", 1:length(fsets), sep=".") ## Kuncheva index stab.fs(fsets=fsets, N=10000, method="kuncheva") ## close to 0 as expected for a random feature selection ## Davis index stab.fs(fsets=fsets, N=10000, method="davis", penalty=1)