implementing 10 cross validation (10CV)

[arwa ali]

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
}

[Stephan van Hulst]

I think you'll get more help with this if you:

Enclose your code in [code]code[/code] tags.

Write out acronyms like PSO and KNN. I have no idea what you're talking about.

Describe the purpose of the algorithm.

Describe the expected output, and what you're getting instead.

Describe why you're stuck.

[arwa ali]

arwa ali wrote:

I am working in particle swarm optimization for feature selection. I use k nearest neighbors  classification algorithm with 10 cross validation for the evaluation. i use the algorithm to achieve feature selection meaning to reduce the number of features , before I use the 10 cross validation the algorithm is quite cheap meaning no high computational cost has been faced, but after turning to 10 cross validation 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
}]