The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | segment_2 |
Description | Segment 2 on Hilbert |
This report file | final/segment_2/segment_2_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/segment_2.dat |
Time of completion | Mon Jun 1 02:37:56 1998 |
Duration of Batch | 11 hours, 11 minutes, 15 seconds |
Random Seed | 706879489 (from clock) |
Average generations per run | 28.30 |
Average failed feature creations per run | 1116.20 |
Average fitness evalutions per run | 74757.10 |
Test Set Improvement | 10.38 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 165 | 82 | 83 | 330 |
1 | 165 | 84 | 81 | 330 |
2 | 165 | 82 | 83 | 330 |
3 | 165 | 82 | 83 | 330 |
4 | 165 | 82 | 83 | 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) | 16.62 | 16.98 | 14.88 |
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1 | 5.80 | 4.85 | 5.19 | 1.000 | 6 | 6 | 23 | ML |
2 | 6.67 | 4.68 | 4.50 | 1.000 | 6 | 4 | 15 | ML |
3 | 7.36 | 6.93 | 8.13 | 1.000 | 8 | 7 | 14 | ML |
4 | 8.23 | 6.24 | 6.40 | 1.000 | 4 | 4 | 9 | ML |
5 | 9.70 | 7.63 | 9.34 | 0.999 | 6 | 10 | 63 | ML |
6 | 6.15 | 4.51 | 5.36 | 1.000 | 9 | 8 | 59 | ML |
7 | 7.71 | 5.37 | 5.54 | 1.000 | 5 | 5 | 16 | ML |
8 | 5.97 | 4.51 | 4.50 | 1.000 | 8 | 11 | 92 | ML |
9 | 7.01 | 5.72 | 6.75 | 1.000 | 7 | 6 | 17 | ML |
10 | 7.97 | 5.55 | 6.40 | 1.000 | 4 | 4 | 10 | ML |
Ave. | 7.26 (1.14 ) | 5.60 (1.01 ) | 6.21 (1.48 ) | 1.00 (0.00 ) | 6.30 (1.62 ) | 6.50 (2.38 ) | 31.80 (27.35 ) | ML |
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Total | |||||||
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Ground Truth |
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81 | 0 | 0 | 0 | 2 | 0 | 0 | 83 |
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0 | 80 | 0 | 1 | 0 | 0 | 0 | 81 | |
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1 | 0 | 75 | 0 | 7 | 0 | 0 | 83 | |
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0 | 0 | 0 | 77 | 6 | 0 | 0 | 83 | |
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0 | 0 | 5 | 3 | 75 | 0 | 0 | 83 | |
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0 | 0 | 0 | 0 | 0 | 82 | 0 | 82 | |
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0 | 0 | 0 | 0 | 1 | 0 | 82 | 83 | |
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82 | 80 | 80 | 81 | 91 | 82 | 82 | 578 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.096 |
Add-Feature Mutation | 0.098 |
Hoist Mutation | 0.096 |
Truncate Mutation | 0.095 |
Swap Mutation | 0.100 |
One-Symbol Mutation | 0.104 |
All-Nodes Mutation | 0.096 |
One-Node Mutation | 0.096 |
Grow Mutation | 0.104 |
High-Level Crossover | 0.115 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 7 |
Max. Generations | 3 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
segment_2_gen.m | Matlab plot generation function |
segment_2_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
segment_2_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
segment_2_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
segment_2_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
segment_2_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
segment_2_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
segment_2_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
segment_2_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
segment_2_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
segment_2_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
segment_2_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
segment_2_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
segment_2_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
segment_2_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_2_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
segment_2_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_2_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
segment_2_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
segment_2_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_2_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_2_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_2_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_2_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_2_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |