Java

I am working in PSO for feature selection. I use KNN algorithm with 10 cross validation for the evaluation. before I use the 10cv the algorithm is quite cheap meaning no high computational cost has been faced, but after turning to 10cv the code is running too slow, sometimes for days. may I know if there is any problem in performing the 10cv. I use the following code to perform 10 cv:

dataset data = FileHandler.loadDataset(new File(dataSetFileName+".csv"), noFeatures, ","); 
                //crossvalidationmat
                  int[][] crossvalidationmat= { 
                    {1,2,3,4,5,6,7,8,9}, 
                    {0,2,3,4,5,6,7,8,9}, 
                    {0,1,3,4,5,6,7,8,9},
                    {0,1,2,4,5,6,7,8,9}, 
                    {0,1,2,3,5,6,7,8,9},
                    {0,1,2,3,4,6,7,8,9},
                    {0,1,2,3,4,5,7,8,9},
                    {0,1,2,3,4,5,6,8,9},
                    {0,1,2,3,4,5,6,7,9},
                    {0,1,2,3,4,5,6,7,8},
                    };
        Dataset[] folds = data.folds((10), new Random(1));
        Dataset training = new DefaultDataset();    //training, testing
        Dataset testing = new DefaultDataset();   
        int[] tr =new int[9];
        int[] te = new int[1]; 
for (int di = 0; di < crossvalidationmat.length; di++) { // start crossvalidation
       System.out.println(crossvalidationmat[di].length);
for (int xj = 0; xj < crossvalidationmat[di].length; xj++) { 
         tr[xj]=crossvalidationmat[di][xj];
         System.out.print(tr[xj]);
    }
    te[0]=di;
    System.out.println("\nTraing  te[0]=di here:  "+te[0]);
        for (int i = 0; i < tr.length; i++) {
            training.addAll(folds[tr[i]]);
        }
        for (int i = 0; i < te.length; i++) {
            testing.addAll(folds[te[i]]);
        }
 Dataset[] foldsTrain = training.folds(numFolds, new Random(1));
//other code
}