The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | segment_1 |
Description | Segment 1 on Gauss |
This report file | final/segment_1/segment_1_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/segment_1.dat |
Time of completion | Mon Jun 1 16:17:30 1998 |
Duration of Batch | 45 hours, 59 minutes, 14 seconds |
Random Seed | 2281703552 (from clock) |
Average generations per run | 32.80 |
Average failed feature creations per run | 1161.90 |
Average fitness evalutions per run | 118756.20 |
Test Set Improvement | 80.80 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 165 | 82 | 83 | 330 |
1 | 165 | 82 | 83 | 330 |
2 | 165 | 82 | 83 | 330 |
3 | 165 | 83 | 82 | 330 |
4 | 165 | 83 | 82 | 330 |
5 | 165 | 83 | 82 | 330 |
6 | 165 | 82 | 83 | 330 |
Total | 1155 | 577 | 578 | 2310 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Maximum Likelihood(ML) | 85.71 | 85.79 | 85.64 |
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1 | 5.97 | 6.76 | 7.61 | 1.000 | 7 | 10 | 88 | ML |
2 | 7.45 | 7.28 | 6.23 | 1.000 | 8 | 10 | 107 | ML |
3 | 5.80 | 6.93 | 6.06 | 1.000 | 9 | 11 | 100 | ML |
4 | 6.67 | 7.28 | 5.88 | 1.000 | 4 | 11 | 182 | ML |
5 | 5.89 | 5.37 | 4.84 | 1.000 | 9 | 6 | 28 | ML |
6 | 9.09 | 9.53 | 9.00 | 1.000 | 6 | 11 | 148 | ML |
7 | 7.01 | 6.76 | 7.61 | 1.000 | 10 | 11 | 162 | ML |
8 | 6.58 | 6.93 | 5.71 | 1.000 | 4 | 4 | 10 | ML |
9 | 7.97 | 8.67 | 7.96 | 1.000 | 7 | 11 | 118 | ML |
10 | 6.93 | 6.93 | 7.44 | 1.000 | 9 | 11 | 305 | ML |
Ave. | 6.94 (0.97 ) | 7.24 (1.07 ) | 6.83 (1.21 ) | 1.00 (0.00 ) | 7.30 (2.00 ) | 9.60 (2.37 ) | 124.80(79.09 ) | ML |
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Total | |||||||
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Ground Truth |
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81 | 0 | 1 | 0 | 1 | 0 | 0 | 83 |
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0 | 81 | 0 | 2 | 0 | 0 | 0 | 83 | |
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1 | 0 | 74 | 0 | 8 | 0 | 0 | 83 | |
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0 | 0 | 1 | 78 | 3 | 0 | 0 | 82 | |
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0 | 0 | 8 | 1 | 73 | 0 | 0 | 82 | |
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0 | 0 | 0 | 2 | 0 | 80 | 0 | 82 | |
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0 | 0 | 0 | 0 | 0 | 0 | 83 | 83 | |
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82 | 81 | 84 | 83 | 85 | 80 | 83 | 578 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.092 |
Add-Feature Mutation | 0.092 |
Hoist Mutation | 0.102 |
Truncate Mutation | 0.097 |
Swap Mutation | 0.101 |
One-Symbol Mutation | 0.101 |
All-Nodes Mutation | 0.100 |
One-Node Mutation | 0.095 |
Grow Mutation | 0.099 |
High-Level Crossover | 0.119 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 5 |
Max. Generations | 5 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
segment_1_gen.m | Matlab plot generation function |
segment_1_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
segment_1_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
segment_1_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
segment_1_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
segment_1_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
segment_1_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
segment_1_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
segment_1_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
segment_1_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
segment_1_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
segment_1_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
segment_1_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
segment_1_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
segment_1_tst_bor_x.pred x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Test set predictions for best-of-run individual from run x |
segment_1_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
segment_1_bogv_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Validation Set Error for run x |
segment_1_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
segment_1_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
segment_1_nftr_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of features per individual for run x |
segment_1_nnode_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of nodes per individual for run x |
segment_1_nint_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of introns per individual for run x |
segment_1_ntrl_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of RAT trials per individual for run x |
segment_1_optimp_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average improvement in fitness due to optimisation for run x |
segment_1_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |