--------------------------------------------------------------Current Dataset: cocomo81
   Run #: 1
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 1.2376
         2. RuleInduction, 1.408
         3. ABE0, 2.9555
         4. 1NN, 7.2537
         5. NeuralNet, 189110525169845340000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 2
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. NeuralNet, 553.89
         2. ABE0, 624.3937
         3. 1NN, 639.2825
         4. RuleInduction, 668.4767
         5. SWRegression, 916.3731
   Run #: 3
      Pre-Processor: none
      Error-Measure: MAR
      Sorted Algorithms:
         1. ABE0, 625.4413
         2. RuleInduction, 689.4017
         3. NeuralNet, 700.1138
         4. 1NN, 790.0286
         5. SWRegression, 819.7801
   Run #: 4
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. 1NN, 0.5196
         2. RuleInduction, 0.55077
         3. NeuralNet, 0.56507
         4. ABE0, 0.63306
         5. SWRegression, 0.73517
   Run #: 5
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. NeuralNet, 553.89
         2. ABE0, 624.3937
         3. 1NN, 639.2825
         4. RuleInduction, 668.4767
         5. SWRegression, 916.3731
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. NeuralNet, 553.89
         2. ABE0, 624.3937
         3. 1NN, 639.2825
         4. RuleInduction, 668.4767
         5. SWRegression, 916.3731
   Run #: 7
      Pre-Processor: none
      Error-Measure: MAR
      Sorted Algorithms:
         1. ABE0, 625.4413
         2. RuleInduction, 689.4017
         3. NeuralNet, 700.1138
         4. 1NN, 790.0286
         5. SWRegression, 819.7801
   Run #: 8
      Pre-Processor: none
      Error-Measure: MMRE
      Sorted Algorithms:
         1. NeuralNet, 1.5614
         2. RuleInduction, 1.5864
         3. ABE0, 2.8584
         4. 1NN, 4.4936
         5. SWRegression, 15.2322
   Run #: 9
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. 1NN, 1.0477
         2. RuleInduction, 1.5864
         3. ABE0, 2.1839
         4. NeuralNet, 2.5481
         5. SWRegression, 26.9493
   Run #: 10
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.55077
         2. 1NN, 0.64149
         3. ABE0, 0.64296
         4. NeuralNet, 0.72414
         5. SWRegression, 0.73385
--------------------------------------------------------------Current Dataset: cocomo81o
   Run #: 1
      Pre-Processor: none
      Error-Measure: MMRE
      Sorted Algorithms:
         1. ABE0, 0.54455
         2. 1NN, 1.108
         3. RuleInduction, 1.2007
         4. NeuralNet, 1.2229
         5. SWRegression, 1.6968
   Run #: 2
      Pre-Processor: none
      Error-Measure: Pred25
      Sorted Algorithms:
         1. ABE0, 0.33333
         2. RuleInduction, 0.29167
         3. 1NN, 0.16667
         4. NeuralNet, 0.16667
         5. SWRegression, 0.125
   Run #: 3
      Pre-Processor: discretize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. NeuralNet, 1.0098
         2. 1NN, 1.0219
         3. ABE0, 1.067
         4. RuleInduction, 1.3422
         5. SWRegression, 6.2529
   Run #: 4
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. ABE0, 0.64199
         2. 1NN, 0.88063
         3. RuleInduction, 1.2007
         4. NeuralNet, 1.3183
         5. SWRegression, 1.8764
   Run #: 5
      Pre-Processor: discretize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. SWRegression, 0.375
         2. ABE0, 0.29167
         3. RuleInduction, 0.25
         4. 1NN, 0.25
         5. NeuralNet, 0.20833
   Run #: 6
      Pre-Processor: none
      Error-Measure: MMRE
      Sorted Algorithms:
         1. ABE0, 0.54455
         2. 1NN, 1.108
         3. RuleInduction, 1.2007
         4. NeuralNet, 1.2229
         5. SWRegression, 1.6968
   Run #: 7
      Pre-Processor: log
      Error-Measure: MAR
      Sorted Algorithms:
         1. ABE0, 26.625
         2. RuleInduction, 34.6846
         3. NeuralNet, 35.3594
         4. SWRegression, 39.2025
         5. 1NN, 42.125
   Run #: 8
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. ABE0, 0.71469
         2. RuleInduction, 0.86176
         3. NeuralNet, 0.87637
         4. 1NN, 0.88335
         5. SWRegression, 5.6657
   Run #: 9
      Pre-Processor: normalize
      Error-Measure: MMER
      Sorted Algorithms:
         1. NeuralNet, 0.61222
         2. ABE0, 0.63136
         3. 1NN, 1.022
         4. RuleInduction, 1.1885
         5. SWRegression, 1.6617
   Run #: 10
      Pre-Processor: discretize
      Error-Measure: MMRE
      Sorted Algorithms:
         1. NeuralNet, 0.53381
         2. 1NN, 0.54072
         3. ABE0, 0.7454
         4. RuleInduction, 0.90727
         5. SWRegression, 0.98778
--------------------------------------------------------------Current Dataset: cocomo81e
   Run #: 1
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. 1NN, 1.2404
         2. RuleInduction, 1.7825
         3. NeuralNet, 2.1518
         4. ABE0, 2.1848
         5. SWRegression, 12.3827
   Run #: 2
      Pre-Processor: normalize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. SWRegression, 0.17857
         2. RuleInduction, 0.17857
         3. NeuralNet, 0.14286
         4. 1NN, 0.071429
         5. ABE0, 0.035714
   Run #: 3
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. NeuralNet, 0.50863
         2. RuleInduction, 0.55546
         3. 1NN, 0.55839
         4. ABE0, 0.6066
         5. SWRegression, 0.62312
   Run #: 4
      Pre-Processor: log
      Error-Measure: MBRE
      Sorted Algorithms:
         1. 1NN, 1.9907
         2. RuleInduction, 2.3181
         3. NeuralNet, 2.3866
         4. ABE0, 3.4055
         5. SWRegression, 12.5102
   Run #: 5
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. SWRegression, 0.54681
         2. RuleInduction, 0.56264
         3. NeuralNet, 0.62159
         4. ABE0, 0.62419
         5. 1NN, 0.62773
   Run #: 6
      Pre-Processor: log
      Error-Measure: MMER
      Sorted Algorithms:
         1. NeuralNet, 0.74342
         2. SWRegression, 0.75061
         3. RuleInduction, 1.0911
         4. 1NN, 1.3087
         5. ABE0, 1.8273
   Run #: 7
      Pre-Processor: log
      Error-Measure: MMER
      Sorted Algorithms:
         1. NeuralNet, 0.74342
         2. SWRegression, 0.75061
         3. RuleInduction, 1.0911
         4. 1NN, 1.3087
         5. ABE0, 1.8273
   Run #: 8
      Pre-Processor: log
      Error-Measure: MBRE
      Sorted Algorithms:
         1. 1NN, 1.9907
         2. RuleInduction, 2.3181
         3. NeuralNet, 2.3866
         4. ABE0, 3.4055
         5. SWRegression, 12.5102
   Run #: 9
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 0.90531
         2. RuleInduction, 1.415
         3. ABE0, 1.9144
         4. NeuralNet, 2.0362
         5. 1NN, 2.6594
   Run #: 10
      Pre-Processor: log
      Error-Measure: MAR
      Sorted Algorithms:
         1. NeuralNet, 745.7951
         2. 1NN, 794.7143
         3. ABE0, 956.3571
         4. SWRegression, 1182.8086
         5. RuleInduction, 1201.786
--------------------------------------------------------------Current Dataset: cocomo81s
   Run #: 1
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 1.4155
         2. RuleInduction, 1.7353
         3. ABE0, 4.0031
         4. 1NN, 44.2385
         5. NeuralNet, 783.5362
   Run #: 2
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. 1NN, 0.49197
         2. NeuralNet, 0.56169
         3. RuleInduction, 0.73167
         4. ABE0, 0.7614
         5. SWRegression, 0.7639
   Run #: 3
      Pre-Processor: normalize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. 1NN, 0.18182
         2. RuleInduction, 0.090909
         3. NeuralNet, 0.090909
         4. SWRegression, 0
         5. ABE0, 0
   Run #: 4
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. 1NN, 0.74108
         2. NeuralNet, 1.7154
         3. ABE0, 6.0548
         4. SWRegression, 8.7016
         5. RuleInduction, 12.4252
   Run #: 5
      Pre-Processor: normalize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. 1NN, 0.65563
         2. ABE0, 0.96203
         3. RuleInduction, 0.98732
         4. NeuralNet, 1.2015
         5. SWRegression, 3.5674
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. NeuralNet, 0.86386
         2. 1NN, 0.86727
         3. ABE0, 0.97562
         4. RuleInduction, 4.8408
         5. SWRegression, 15.8931
   Run #: 7
      Pre-Processor: none
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. 1NN, 0.60875
         2. NeuralNet, 0.64151
         3. RuleInduction, 0.73167
         4. SWRegression, 0.73606
         5. ABE0, 0.7855
   Run #: 8
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. 1NN, 0.74108
         2. NeuralNet, 1.7154
         3. ABE0, 6.0548
         4. SWRegression, 8.7016
         5. RuleInduction, 12.4252
   Run #: 9
      Pre-Processor: none
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. 1NN, 0.65563
         2. NeuralNet, 0.65563
         3. ABE0, 0.96203
         4. RuleInduction, 0.98732
         5. SWRegression, 3.5674
   Run #: 10
      Pre-Processor: none
      Error-Measure: MMRE
      Sorted Algorithms:
         1. NeuralNet, 0.75972
         2. 1NN, 1.0822
         3. ABE0, 6.8198
         4. SWRegression, 7.3907
         5. RuleInduction, 12.4252
--------------------------------------------------------------Current Dataset: nasa93
   Run #: 1
      Pre-Processor: discretize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.32258
         2. ABE0, 0.23656
         3. 1NN, 0.22581
         4. NeuralNet, 0.21505
         5. SWRegression, 0.15054
   Run #: 2
      Pre-Processor: discretize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.37677
         2. 1NN, 0.47822
         3. NeuralNet, 0.48044
         4. ABE0, 0.49426
         5. SWRegression, 0.55783
   Run #: 3
      Pre-Processor: none
      Error-Measure: Pred25
      Sorted Algorithms:
         1. 1NN, 0.31183
         2. RuleInduction, 0.29032
         3. SWRegression, 0.17204
         4. NeuralNet, 0.15054
         5. ABE0, 0.13978
   Run #: 4
      Pre-Processor: discretize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.37677
         2. 1NN, 0.47822
         3. NeuralNet, 0.48044
         4. ABE0, 0.49426
         5. SWRegression, 0.55783
   Run #: 5
      Pre-Processor: none
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. RuleInduction, 0.46133
         2. 1NN, 0.56883
         3. ABE0, 0.64444
         4. SWRegression, 0.71066
         5. NeuralNet, 0.85618
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. RuleInduction, 387.7106
         2. ABE0, 431.2892
         3. NeuralNet, 518.2373
         4. SWRegression, 539.2165
         5. 1NN, 553.043
   Run #: 7
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 0.71468
         2. RuleInduction, 0.78938
         3. NeuralNet, 1.2879
         4. ABE0, 1.3947
         5. 1NN, 1.6726
   Run #: 8
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 0.71468
         2. RuleInduction, 0.78938
         3. NeuralNet, 1.2879
         4. ABE0, 1.3947
         5. 1NN, 1.6726
   Run #: 9
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. 1NN, 0.77379
         2. ABE0, 1.1035
         3. RuleInduction, 1.2202
         4. NeuralNet, 1.6542
         5. SWRegression, 5.963
   Run #: 10
      Pre-Processor: log
      Error-Measure: MAR
      Sorted Algorithms:
         1. RuleInduction, 393.1418
         2. NeuralNet, 434.5964
         3. 1NN, 452.8667
         4. ABE0, 457.6634
         5. SWRegression, 467.4006
--------------------------------------------------------------Current Dataset: nasa93_center_1
   Run #: 1
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.2311
         2. SWRegression, 0.29555
         3. 1NN, 0.3044
         4. ABE0, 0.4377
         5. NeuralNet, 0.55072
   Run #: 2
      Pre-Processor: normalize
      Error-Measure: MMRE
      Sorted Algorithms:
         1. RuleInduction, 0.33265
         2. 1NN, 0.37657
         3. SWRegression, 0.38305
         4. ABE0, 0.49048
         5. NeuralNet, 1.1114
   Run #: 3
      Pre-Processor: normalize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. RuleInduction, 0.16759
         2. 1NN, 0.26667
         3. SWRegression, 0.31095
         4. ABE0, 0.33333
         5. NeuralNet, 0.82104
   Run #: 4
      Pre-Processor: discretize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. NeuralNet, 1.4992
         2. 1NN, 1.8201
         3. RuleInduction, 2.057
         4. ABE0, 2.5316
         5. SWRegression, 3.9129
   Run #: 5
      Pre-Processor: none
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.58333
         2. 1NN, 0.5
         3. NeuralNet, 0.41667
         4. ABE0, 0.33333
         5. SWRegression, 0.25
   Run #: 6
      Pre-Processor: log
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.58333
         2. 1NN, 0.33333
         3. ABE0, 0.33333
         4. NeuralNet, 0.25
         5. SWRegression, 0.16667
   Run #: 7
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 0.26395
         2. SWRegression, 0.41072
         3. 1NN, 1.2669
         4. ABE0, 1.7444
         5. NeuralNet, 27.3398
   Run #: 8
      Pre-Processor: none
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.2311
         2. SWRegression, 0.29555
         3. 1NN, 0.3044
         4. NeuralNet, 0.36569
         5. ABE0, 0.4377
   Run #: 9
      Pre-Processor: none
      Error-Measure: MAR
      Sorted Algorithms:
         1. RuleInduction, 32.4524
         2. SWRegression, 55.1012
         3. NeuralNet, 65.7539
         4. 1NN, 73.0833
         5. ABE0, 95.9167
   Run #: 10
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 1.2589
         2. NeuralNet, 1.3368
         3. ABE0, 1.649
         4. 1NN, 1.6752
         5. SWRegression, 3.4224
--------------------------------------------------------------Current Dataset: nasa93_center_2
   Run #: 1
      Pre-Processor: discretize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. 1NN, 0.2973
         2. RuleInduction, 0.27027
         3. NeuralNet, 0.24324
         4. ABE0, 0.13514
         5. SWRegression, 0.054054
   Run #: 2
      Pre-Processor: log
      Error-Measure: Pred25
      Sorted Algorithms:
         1. 1NN, 0.56757
         2. RuleInduction, 0.43243
         3. SWRegression, 0.13514
         4. NeuralNet, 0.13514
         5. ABE0, 0.13514
   Run #: 3
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.31148
         2. 1NN, 0.3382
         3. SWRegression, 0.38176
         4. ABE0, 0.54384
         5. NeuralNet, 0.5641
   Run #: 4
      Pre-Processor: normalize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. 1NN, 0.28571
         2. RuleInduction, 0.3
         3. SWRegression, 0.46108
         4. ABE0, 0.66667
         5. NeuralNet, 0.85994
   Run #: 5
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. 1NN, 0.27192
         2. RuleInduction, 0.31148
         3. NeuralNet, 0.47464
         4. SWRegression, 0.48766
         5. ABE0, 0.52493
   Run #: 6
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 0.56278
         2. 1NN, 1.7533
         3. ABE0, 1.7773
         4. SWRegression, 3.5843
         5. NeuralNet, 120964032226102890000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 7
      Pre-Processor: discretize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. NeuralNet, 2.1175
         2. ABE0, 2.673
         3. RuleInduction, 3.2688
         4. 1NN, 3.5099
         5. SWRegression, 4.6383
   Run #: 8
      Pre-Processor: discretize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. NeuralNet, 0.57148
         2. 1NN, 0.64
         3. RuleInduction, 0.66421
         4. ABE0, 0.66667
         5. SWRegression, 2.3118
   Run #: 9
      Pre-Processor: discretize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.42837
         2. NeuralNet, 0.45567
         3. 1NN, 0.47355
         4. ABE0, 0.52704
         5. SWRegression, 0.63915
   Run #: 10
      Pre-Processor: none
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. 1NN, 0.28571
         2. RuleInduction, 0.3
         3. SWRegression, 0.46108
         4. NeuralNet, 0.4868
         5. ABE0, 0.66667
--------------------------------------------------------------Current Dataset: nasa93_center_5
   Run #: 1
      Pre-Processor: normalize
      Error-Measure: MMER
      Sorted Algorithms:
         1. NeuralNet, 0.73842
         2. RuleInduction, 0.74154
         3. ABE0, 1.0298
         4. 1NN, 1.7781
         5. SWRegression, 3.2182
   Run #: 2
      Pre-Processor: normalize
      Error-Measure: MMER
      Sorted Algorithms:
         1. NeuralNet, 0.73842
         2. RuleInduction, 0.74154
         3. ABE0, 1.0298
         4. 1NN, 1.7781
         5. SWRegression, 3.2182
   Run #: 3
      Pre-Processor: normalize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. RuleInduction, 1.2598
         2. ABE0, 1.3649
         3. NeuralNet, 1.73
         4. 1NN, 2.3054
         5. SWRegression, 4.0535
   Run #: 4
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. 1NN, 0.72064
         2. ABE0, 0.89346
         3. RuleInduction, 0.90491
         4. SWRegression, 1.2244
         5. NeuralNet, 1.6958
   Run #: 5
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 0.74154
         2. ABE0, 1.0298
         3. 1NN, 1.7781
         4. SWRegression, 3.2182
         5. NeuralNet, 737951946283126900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. NeuralNet, 0.54065
         2. 1NN, 0.56883
         3. ABE0, 0.60317
         4. SWRegression, 0.71448
         5. RuleInduction, 0.72134
   Run #: 7
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. 1NN, 675.2359
         2. NeuralNet, 686.9127
         3. ABE0, 695.6487
         4. SWRegression, 757.8986
         5. RuleInduction, 968.1723
   Run #: 8
      Pre-Processor: normalize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.35897
         2. NeuralNet, 0.23077
         3. SWRegression, 0.20513
         4. ABE0, 0.20513
         5. 1NN, 0.17949
   Run #: 9
      Pre-Processor: normalize
      Error-Measure: MAR
      Sorted Algorithms:
         1. SWRegression, 589.9467
         2. ABE0, 598.1128
         3. RuleInduction, 641.1933
         4. NeuralNet, 683.747
         5. 1NN, 747.3769
   Run #: 10
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. 1NN, 0.38431
         2. RuleInduction, 0.38664
         3. SWRegression, 0.4197
         4. ABE0, 0.49542
         5. NeuralNet, 0.53545
--------------------------------------------------------------Current Dataset: desharnais
   Run #: 1
      Pre-Processor: discretize
      Error-Measure: MMRE
      Sorted Algorithms:
         1. RuleInduction, 0.48932
         2. SWRegression, 0.64588
         3. ABE0, 0.74403
         4. NeuralNet, 0.77355
         5. 1NN, 0.83806
   Run #: 2
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.31485
         2. NeuralNet, 0.33077
         3. SWRegression, 0.35337
         4. 1NN, 0.3677
         5. ABE0, 0.36943
   Run #: 3
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.31485
         2. NeuralNet, 0.33077
         3. SWRegression, 0.35337
         4. 1NN, 0.3677
         5. ABE0, 0.36943
   Run #: 4
      Pre-Processor: none
      Error-Measure: MMRE
      Sorted Algorithms:
         1. SWRegression, 0.5103
         2. RuleInduction, 0.52936
         3. ABE0, 0.59148
         4. 1NN, 0.6831
         5. NeuralNet, 0.97656
   Run #: 5
      Pre-Processor: normalize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. SWRegression, 0.31389
         2. RuleInduction, 0.32947
         3. ABE0, 0.35343
         4. 1NN, 0.42159
         5. NeuralNet, 0.54209
   Run #: 6
      Pre-Processor: normalize
      Error-Measure: MAR
      Sorted Algorithms:
         1. SWRegression, 2010.7695
         2. RuleInduction, 2076.1504
         3. ABE0, 2188.5432
         4. 1NN, 2772.4198
         5. NeuralNet, 2854.9071
   Run #: 7
      Pre-Processor: none
      Error-Measure: MAR
      Sorted Algorithms:
         1. SWRegression, 2010.7695
         2. RuleInduction, 2076.1504
         3. ABE0, 2188.5432
         4. 1NN, 2772.4198
         5. NeuralNet, 4988.0617
   Run #: 8
      Pre-Processor: log
      Error-Measure: MMRE
      Sorted Algorithms:
         1. RuleInduction, 0.52936
         2. SWRegression, 0.61737
         3. NeuralNet, 0.7225
         4. ABE0, 0.73832
         5. 1NN, 0.80337
   Run #: 9
      Pre-Processor: normalize
      Error-Measure: MAR
      Sorted Algorithms:
         1. SWRegression, 2010.7695
         2. RuleInduction, 2076.1504
         3. ABE0, 2188.5432
         4. 1NN, 2772.4198
         5. NeuralNet, 2854.9071
   Run #: 10
      Pre-Processor: none
      Error-Measure: MBRE
      Sorted Algorithms:
         1. SWRegression, 0.64575
         2. RuleInduction, 0.67046
         3. ABE0, 0.7196
         4. 1NN, 0.91985
         5. NeuralNet, 987625111449501390000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
--------------------------------------------------------------Current Dataset: desharnaisL1
   Run #: 1
      Pre-Processor: normalize
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 0.44233
         2. ABE0, 0.46124
         3. RuleInduction, 0.48665
         4. NeuralNet, 0.53547
         5. 1NN, 0.58602
   Run #: 2
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 0.44233
         2. ABE0, 0.46124
         3. RuleInduction, 0.48665
         4. 1NN, 0.58602
         5. NeuralNet, 1739079000595860800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 3
      Pre-Processor: none
      Error-Measure: MBRE
      Sorted Algorithms:
         1. SWRegression, 0.56415
         2. ABE0, 0.63282
         3. RuleInduction, 0.65358
         4. 1NN, 0.84206
         5. NeuralNet, 1739079000595860800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 4
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. SWRegression, 0.29557
         2. ABE0, 0.30495
         3. RuleInduction, 0.31807
         4. 1NN, 0.36169
         5. NeuralNet, 0.37618
   Run #: 5
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. SWRegression, 0.44233
         2. ABE0, 0.46124
         3. RuleInduction, 0.48665
         4. 1NN, 0.58602
         5. NeuralNet, 1739079000595860800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 6
      Pre-Processor: none
      Error-Measure: MBRE
      Sorted Algorithms:
         1. SWRegression, 0.56415
         2. ABE0, 0.63282
         3. RuleInduction, 0.65358
         4. 1NN, 0.84206
         5. NeuralNet, 1739079000595860800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 7
      Pre-Processor: log
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.3913
         2. ABE0, 0.3913
         3. SWRegression, 0.36957
         4. 1NN, 0.34783
         5. NeuralNet, 0.28261
   Run #: 8
      Pre-Processor: normalize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. SWRegression, 0.56415
         2. ABE0, 0.63282
         3. RuleInduction, 0.65358
         4. 1NN, 0.84206
         5. NeuralNet, 0.90719
   Run #: 9
      Pre-Processor: normalize
      Error-Measure: MAR
      Sorted Algorithms:
         1. SWRegression, 2013.9577
         2. RuleInduction, 2333.438
         3. ABE0, 2428.3043
         4. NeuralNet, 2974.559
         5. 1NN, 3127.1522
   Run #: 10
      Pre-Processor: log
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.3913
         2. ABE0, 0.3913
         3. SWRegression, 0.36957
         4. 1NN, 0.34783
         5. NeuralNet, 0.28261
--------------------------------------------------------------Current Dataset: desharnaisL2
   Run #: 1
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.23897
         2. SWRegression, 0.25221
         3. ABE0, 0.29884
         4. 1NN, 0.32212
         5. NeuralNet, 0.3516
   Run #: 2
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 0.34186
         2. ABE0, 0.42428
         3. NeuralNet, 0.53068
         4. 1NN, 0.63022
         5. SWRegression, 1.7435
   Run #: 3
      Pre-Processor: normalize
      Error-Measure: MAR
      Sorted Algorithms:
         1. RuleInduction, 1398.5967
         2. SWRegression, 1456.2534
         3. ABE0, 1802.64
         4. 1NN, 1881.6
         5. NeuralNet, 2237.7083
   Run #: 4
      Pre-Processor: discretize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.27001
         2. ABE0, 0.30639
         3. NeuralNet, 0.35297
         4. SWRegression, 0.38455
         5. 1NN, 0.38655
   Run #: 5
      Pre-Processor: normalize
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 0.30506
         2. SWRegression, 0.31551
         3. ABE0, 0.39126
         4. NeuralNet, 0.45043
         5. 1NN, 0.45445
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. RuleInduction, 0.28739
         2. ABE0, 0.30754
         3. SWRegression, 0.42825
         4. NeuralNet, 0.54815
         5. 1NN, 0.58248
   Run #: 7
      Pre-Processor: log
      Error-Measure: MBRE
      Sorted Algorithms:
         1. RuleInduction, 0.36045
         2. SWRegression, 0.40057
         3. NeuralNet, 0.40665
         4. ABE0, 0.46733
         5. 1NN, 0.59368
   Run #: 8
      Pre-Processor: normalize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. RuleInduction, 0.36045
         2. SWRegression, 0.39805
         3. ABE0, 0.4945
         4. 1NN, 0.58403
         5. NeuralNet, 0.7007
   Run #: 9
      Pre-Processor: log
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. RuleInduction, 0.23897
         2. SWRegression, 0.24726
         3. NeuralNet, 0.25592
         4. ABE0, 0.27413
         5. 1NN, 0.33342
   Run #: 10
      Pre-Processor: log
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. ABE0, 0.24846
         2. RuleInduction, 0.26133
         3. SWRegression, 0.27356
         4. NeuralNet, 0.28261
         5. 1NN, 0.37057
--------------------------------------------------------------Current Dataset: desharnaisL3
   Run #: 1
      Pre-Processor: log
      Error-Measure: MBRE
      Sorted Algorithms:
         1. ABE0, 0.68689
         2. 1NN, 0.71526
         3. NeuralNet, 0.72624
         4. SWRegression, 0.74085
         5. RuleInduction, 1.3961
   Run #: 2
      Pre-Processor: discretize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. 1NN, 0.3
         2. RuleInduction, 0.2
         3. NeuralNet, 0.2
         4. ABE0, 0.2
         5. SWRegression, 0
   Run #: 3
      Pre-Processor: log
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. ABE0, 0.34548
         2. 1NN, 0.3623
         3. NeuralNet, 0.48969
         4. SWRegression, 0.52622
         5. RuleInduction, 0.94572
   Run #: 4
      Pre-Processor: discretize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. 1NN, 0.3
         2. RuleInduction, 0.2
         3. NeuralNet, 0.2
         4. ABE0, 0.2
         5. SWRegression, 0
   Run #: 5
      Pre-Processor: none
      Error-Measure: MMER
      Sorted Algorithms:
         1. ABE0, 0.60981
         2. 1NN, 0.69062
         3. RuleInduction, 0.8284
         4. SWRegression, 0.96363
         5. NeuralNet, 12057259467281273000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: MMRE
      Sorted Algorithms:
         1. ABE0, 0.44556
         2. 1NN, 0.66492
         3. NeuralNet, 0.67449
         4. RuleInduction, 1.0607
         5. SWRegression, 1.2203
   Run #: 7
      Pre-Processor: none
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. ABE0, 0.28728
         2. 1NN, 0.39812
         3. RuleInduction, 0.49294
         4. SWRegression, 0.51301
         5. NeuralNet, 0.99929
   Run #: 8
      Pre-Processor: discretize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. ABE0, 0.36662
         2. 1NN, 0.36964
         3. NeuralNet, 0.37936
         4. RuleInduction, 0.49294
         5. SWRegression, 0.62075
   Run #: 9
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. ABE0, 968
         2. RuleInduction, 1246.1111
         3. NeuralNet, 1246.7268
         4. 1NN, 1258.9
         5. SWRegression, 1557.2263
   Run #: 10
      Pre-Processor: log
      Error-Measure: MBRE
      Sorted Algorithms:
         1. ABE0, 0.68689
         2. 1NN, 0.71526
         3. NeuralNet, 0.72624
         4. SWRegression, 0.74085
         5. RuleInduction, 1.3961
--------------------------------------------------------------Current Dataset: sdr
   Run #: 1
      Pre-Processor: none
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. NeuralNet, 0.4937
         2. 1NN, 0.5
         3. ABE0, 0.5
         4. RuleInduction, 0.55417
         5. SWRegression, 1.7785
   Run #: 2
      Pre-Processor: discretize
      Error-Measure: MAR
      Sorted Algorithms:
         1. ABE0, 26.5417
         2. RuleInduction, 38.1543
         3. NeuralNet, 46.8086
         4. 1NN, 50
         5. SWRegression, 62.611
   Run #: 3
      Pre-Processor: discretize
      Error-Measure: MMER
      Sorted Algorithms:
         1. RuleInduction, 1.5782
         2. SWRegression, 1.7294
         3. NeuralNet, 3.0029
         4. ABE0, 9.6326
         5. 1NN, 9.7002
   Run #: 4
      Pre-Processor: none
      Error-Measure: Pred25
      Sorted Algorithms:
         1. RuleInduction, 0.41667
         2. 1NN, 0.33333
         3. ABE0, 0.29167
         4. NeuralNet, 0.16667
         5. SWRegression, 0.083333
   Run #: 5
      Pre-Processor: log
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. NeuralNet, 0.43352
         2. RuleInduction, 0.56333
         3. 1NN, 0.69514
         4. ABE0, 0.70175
         5. SWRegression, 1.8834
   Run #: 6
      Pre-Processor: discretize
      Error-Measure: Pred25
      Sorted Algorithms:
         1. ABE0, 0.45833
         2. RuleInduction, 0.29167
         3. 1NN, 0.29167
         4. NeuralNet, 0.25
         5. SWRegression, 0.083333
   Run #: 7
      Pre-Processor: log
      Error-Measure: MBRE
      Sorted Algorithms:
         1. NeuralNet, 1.4434
         2. 1NN, 1.9371
         3. RuleInduction, 4.4515
         4. SWRegression, 6.4988
         5. ABE0, 10.8008
   Run #: 8
      Pre-Processor: discretize
      Error-Measure: MBRE
      Sorted Algorithms:
         1. RuleInduction, 2.7792
         2. NeuralNet, 4.9271
         3. SWRegression, 8.9209
         4. ABE0, 9.8407
         5. 1NN, 11.9612
   Run #: 9
      Pre-Processor: normalize
      Error-Measure: MIBRE
      Sorted Algorithms:
         1. 1NN, 0.45933
         2. ABE0, 0.50144
         3. RuleInduction, 0.50261
         4. NeuralNet, 0.59231
         5. SWRegression, 0.65724
   Run #: 10
      Pre-Processor: discretize
      Error-Measure: MDMRE
      Sorted Algorithms:
         1. ABE0, 0.33333
         2. RuleInduction, 0.54765
         3. 1NN, 0.74474
         4. NeuralNet, 0.74474
         5. SWRegression, 3.6905