# Ensemble Objects for Classification This function provides for the creation, and storage of an ensemble of simpler classifiers. Right now, only a single type of classifier is available; this shall be fixed in the future. ```r ensemble(dat, train, test, labs, bel.type = c('kde', 'knn', 'jit'), bel_options) ``` ## Arguments - `dat`: The full data matrix, including both test and train rows. - `train`: A vector containing the row numbers (or names) of the training data in the matrix `dat`. - `test`: A vector containing the row numbers (or names) of the test data in the matrix `dat`. - `labs`: Labels for the training data; must be of the same length as `train`. - `bel.type`: The type of belief function to build the Ensemble. For more details, see help on Belief. - `bel_options`: The options list that should be passed to the belief function. ## Details The simpler classifiers must work in 2-dimensions projections of the data. ## Returns Returns a list of functions which can be used to query and / or manipulate the created `ensemble` object. - **try.matrices**: Takes a single argument `n` which is the number of matrices you want to try. - **get.bpamats**: Returns a list of bpa.mat objects representing the classifications recieved from each projection. ## Author(s) Mohit Dayal ## Examples ```r data(cancer) table(cancer$V2) colnames(cancer)[1:2] <- c('id', 'type') cancer.d <- as.matrix(cancer[,3:32]) labs <- cancer$type test_size <- floor(0.15*nrow(cancer.d)) train <- sample(1:nrow(cancer.d), size = nrow(cancer.d) - test_size) test <- which(!(1:569 %in% train)) truelabs = labs[test] e <- ensemble(dat = cancer.d, labs = labs[train], train = train, test = test, bel.type = 'kde', bel_options = list(coef = 0.90)) ##Try more matrices than that in real life! ##Also increase the mc.cores parameter if you have more cores! e$try.matrices(n = 3, mc.cores = 1) y <- e$get.bpamats() length(y) ##Can see results from each projection ##b.1 <- bpamat(mat = y[[1]]$get.mat()) ##b.2 <- bpamat(mat = y[[2]]$get.mat()) ##b.12b <- combine.bpa.mat.bs(b.1, b.2) ##b.12d <- combine.bpa.mat.ds(b.1, b.2) ##b.12b$get.classify() ##b.12d$get.classify() ##All the results ##b.n <- lapply(y, function(x) x$get.classify()) ##allb <- combine.bpa.mat.list.bs(e$get.bpamats()) ##alld <- combine.bpa.mat.list.ds(e$get.bpamats()) ##fn1 <- function(x) ## table(truelabs, x$get.classify()) ##fn2 <- function(x) ## { ## tmp <- table(truelabs, x$get.classify()) ## 100 *sum(diag(tmp)) / sum(tmp) ## } ##fn1(allb) ##fn2(allb) ##fn1(alld) ##fn2(alld) ``` ## See Also belief

ensemble function