public static void Test2()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
IEnsembleClassificationStrategy majorityVote = new MajorityVoteStrategy();
IEnsembleClassificationStrategy weightedVote = new WeightedVoteStrategy();
DataMining.Data.Dataset[] tables = BatchTest2.LoadTrainingAndTestingData("audiology", 0);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
//EnsembleClassifier ensemble = SingleTest.CreateGKAntIBMinerClassifier_ClassBaseWeights_Ensemble(trainingSet);
GaussianKernelEstimator GCE = new GaussianKernelEstimator(-0.5, new DefaultDistanceMeasure(1), trainingSet);
double quality = 0;
quality = SingleTest.TestClassifier(GCE, testingSet, accuracyMeasure);
Console.WriteLine("Accuracy=" + quality);
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
public static void Test1()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
IEnsembleClassificationStrategy majorityVote = new MajorityVoteStrategy();
IEnsembleClassificationStrategy weightedVote = new WeightedVoteStrategy();
DataMining.Data.Dataset[] tables = BatchTest2.LoadTrainingAndTestingData("audiology", 0);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
//EnsembleClassifier ensemble = SingleTest.CreateGKAntIBMinerClassifier_ClassBaseWeights_Ensemble(trainingSet);
EnsembleClassifier ensemble = SingleTest.CreateNCCAntIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet);
//EnsembleClassifier ensemble = SingleTest.CreateKNNAntIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet,false);
double quality1 = 0;
double quality2 = 0;
double quality3 = 0;
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
ensemble.Stratgy = majorityVote;
quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
quality1 = Math.Round((quality1 / 1) * 100, 2);
quality2 = Math.Round((quality2 / 1) * 100, 2);
quality3 = Math.Round((quality3 / 1) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("ACO-GKC-CB-CB: Accuracy=" + quality1);
Console.WriteLine("ACO-GKC-CB-ens-MV: Accuracy=" + quality2);
Console.WriteLine("ACO-GKC-CB-ens-WV: Accuracy=" + quality3);
//Console.WriteLine(((KNearestNeighbours)ensemble[0]).KNeighbours.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
public static void RunPSOIBL()
{
AccuracyMeasure accuracyMeasure = new AccuracyMeasure();
IEnsembleClassificationStrategy majorityVote = new MajorityVoteStrategy();
IEnsembleClassificationStrategy weightedVote = new WeightedVoteStrategy();
foreach (string dataset in GetDatasetFolds(DatasetNamesFile))
{
//----------------------------------------
Console.WriteLine("Data Table:" + dataset);
//----------------------------------------
#region PSO-KNN-CB
try
{
double quality1 = 0;
//double quality2 = 0;
double quality3 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
EnsembleClassifier ensemble = SingleTest.CreateKNNPSOIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet, false);
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
ensemble.Stratgy = majorityVote;
//quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
// quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("PSO-KNN-CB: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "PSO-KNN-CB", quality1.ToString());
//Console.WriteLine("PSO-KNN-CB-ens-MV: " + dataset + " - Accuracy=" + quality2);
//SaveResults(dataset, "PSO-KNN-CB-ens-MV",quality2.ToString());
Console.WriteLine("PSO-KNN-CB-ens-WV: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "PSO-KNN-CB-ens-WV", quality3.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
catch (Exception ex)
{
// LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
#region PSO-KNN-CB-WV
//try
//{
// double quality1 = 0;
// double quality2 = 0;
// double quality3 = 0;
// for (_currentFold = 0; _currentFold < _folds; _currentFold++)
// {
// //----------------------------------------
// Console.WriteLine("Fold:" + _currentFold.ToString());
// //----------------------------------------
// DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
// DataMining.Data.Dataset trainingSet = tables[0];
// DataMining.Data.Dataset testingSet = tables[1];
// EnsembleClassifier ensemble = SingleTest.CreateKNNPSOIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet, true);
// quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
// ensemble.Stratgy = majorityVote;
// quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
// ensemble.Stratgy = weightedVote;
// quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
// }
// quality1 = Math.Round((quality1 / _folds) * 100, 2);
// quality2 = Math.Round((quality2 / _folds) * 100, 2);
// quality3 = Math.Round((quality3 / _folds) * 100, 2);
// //------------------------------------------------------------------
// Console.WriteLine("PSO-KNN-CB-WV: " + dataset + " - Accuracy=" + quality1);
// SaveResults(dataset, "PSO-KNN-CB-WV", k.ToString(), quality1.ToString());
// Console.WriteLine("PSO-KNN-CB-WV-ens-MV: " + dataset + " - Accuracy=" + quality2);
// SaveResults(dataset, "PSO-KNN-CB-WV-ens-MV", k.ToString(), quality2.ToString());
// Console.WriteLine("PSO-KNN-CB-WV-ens-WV: " + dataset + " - Accuracy=" + quality3);
// SaveResults(dataset, "PSO-KNN-CB-WV-ens-WV", k.ToString(), quality3.ToString());
// Console.WriteLine("-------------------------------------------");
// Console.WriteLine("-------------------------------------------");
// Console.WriteLine("-------------------------------------------");
//}
//catch (Exception ex)
//{
// LogError(ex);
// // Console.WriteLine(ex.Message);
//}
#endregion
#region PSO-NCC-CB
try
{
double quality1 = 0;
//double quality2 = 0;
double quality3 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
EnsembleClassifier ensemble = SingleTest.CreateNCCPSOIBMinerClassifier_ClassBasedWeights_Ensemble(trainingSet);
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
//ensemble.Stratgy = majorityVote;
//quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
//quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("PSO-NCC-CB: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "PSO-NNC-CB", quality1.ToString());
//Console.WriteLine("PSO-NNC-CB-ens-MV: " + dataset + " - Accuracy=" + quality2);
//SaveResults(dataset, "PSO-NNC-CB-ens-MV", quality2.ToString());
Console.WriteLine("PSO-NNC-CB-ens-WV: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "PSO-NNC-CB-ens-WV", quality3.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
catch (Exception ex)
{
LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
#region PSO-GKC-CB
try
{
double quality1 = 0;
//double quality2 = 0;
double quality3 = 0;
for (_currentFold = 0; _currentFold < _folds; _currentFold++)
{
//----------------------------------------
Console.WriteLine("Fold:" + _currentFold.ToString());
//----------------------------------------
DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold);
DataMining.Data.Dataset trainingSet = tables[0];
DataMining.Data.Dataset testingSet = tables[1];
EnsembleClassifier ensemble = SingleTest.CreateGKPSOIBMinerClassifier_ClassBaseWeights_Ensemble(trainingSet);
quality1 += SingleTest.TestClassifier(ensemble[0], testingSet, accuracyMeasure);
//ensemble.Stratgy = majorityVote;
//quality2 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
ensemble.Stratgy = weightedVote;
quality3 += SingleTest.TestClassifier(ensemble, testingSet, accuracyMeasure);
}
quality1 = Math.Round((quality1 / _folds) * 100, 2);
//quality2 = Math.Round((quality2 / _folds) * 100, 2);
quality3 = Math.Round((quality3 / _folds) * 100, 2);
//------------------------------------------------------------------
Console.WriteLine("PSO-GKC-CB-CB: " + dataset + " - Accuracy=" + quality1);
SaveResults(dataset, "PSO-GKC-CB", quality1.ToString());
//Console.WriteLine("PSO-GKC-CB-ens-MV: " + dataset + " - Accuracy=" + quality2);
//SaveResults(dataset, "PSO-GKC-CB-ens-MV", quality2.ToString());
Console.WriteLine("PSO-GKC-CB-ens-WV: " + dataset + " - Accuracy=" + quality3);
SaveResults(dataset, "PSO-GKC-CB-ens-WV", quality3.ToString());
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
Console.WriteLine("-------------------------------------------");
}
catch (Exception ex)
{
LogError(ex);
// Console.WriteLine(ex.Message);
}
#endregion
}
}
