PCDSL: The Proximity Catch Digraphs for Statistical Learning
Artür Manukyan, Elvan Ceyhan
Classification methods based on Proximity Catch Digraphs. Proximity Catch Digraphs (PCDs) are special types of proximity graphs.
http://webhome.auburn.edu/~ezc0066/PCDwebpage/index.html
library(devtools)
devtools::install_github("Artur-man/PCDSL")# input parameters
ntest <- 100 # test data size for each class
nx <- 300 # training data size of x class (majority)
r <- 0.1 # Imbalance Ratio
de <- 0.5 # delta, the overlapping parameter
dimx <- 2 # number of dimensions
# training the classifier
set.seed(1)
x0 <- matrix(runif(dimx*nx,0,1),nrow=nx)
x1 <- matrix(runif(dimx*nx*r,de,1+de),nrow=nx*r)
x <- rbind(x0,x1)
classes <- rep(1:2,c(nx,nx*r))
graph_pcd <- pcd_classifier(x,classes,map="pe",p_pcd=1)
# testing
tx0 <- matrix(runif(dimx*ntest,0,1),nrow=ntest)
tx1 <- matrix(runif(dimx*ntest,de,1+de),nrow=ntest)
tx <- rbind(tx0,tx1)
tclsdata <- rep(1:2,rep(ntest,2))
predicted_pcd_tx <- pcd_classify(tx,graph_pcd)