Nothing

```
#' @title Decision Forest algorithm: Feature Selection in pre-processing
#' @description Decision Forest algorithm: feature selection for two-class predictions,
#' kept statistical significant features pass the t-test
#'
#'
#' @param X Training Dataset
#' @param Y Training Labels
#' @param p_val Correlation Coefficient threshold to filter out high correlated features; default is 0.95
#'
#' @return Keep_feat: qualified features in data matrix after filtering
#' @export
#'
#' @examples
#' ##data(iris)
#' X = iris[iris[,5]!="setosa",1:4]
#' Y = iris[iris[,5]!="setosa",5]
#' used_feat = DF_dataFs(X, Y)
DF_dataFs = function (X, Y, p_val=0.05){
Y = factor(Y)
if (length(levels(Y))!=2){
stop("Not 2-class analysis! Cannot perform t-test for feature selection. Exit without changes ... \n")
return (1:ncol(X))
}else{
cat(paste("Doing Feature selection based on Training dataset: p-value < ",p_val," \n",sep=""))
p_value = DF_calp(X,Y)
used_feat = which(p_value<=p_val)
# X_new = X[,used_feat]
cat(paste(length(used_feat)," of ",length(p_value)," features remained! \n",sep=""))
return(used_feat)
}
}
```

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