The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
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Batch Details
Batch run name | segment_3 |
Description | Segment 3 on Hilbert |
This report file | final/segment_3/segment_3_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/segment_3.dat |
Time of completion | Tue Jun 2 05:46:15 1998 |
Duration of Batch | 20 hours, 33 minutes, 37 seconds |
Random Seed | 578978304 (from clock) |
Average generations per run | 33.40 |
Average failed feature creations per run | 1164.90 |
Average fitness evalutions per run | 106907.50 |
Test Set Improvement | 12.80 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 165 | 82 | 83 | 330 |
1 | 165 | 82 | 83 | 330 |
2 | 165 | 83 | 82 | 330 |
3 | 165 | 83 | 82 | 330 |
4 | 165 | 82 | 83 | 330 |
5 | 165 | 82 | 83 | 330 |
6 | 165 | 83 | 82 | 330 |
Total | 1155 | 577 | 578 | 2310 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Maximum Likelihood(ML) | 16.19 | 17.85 | 19.20 |
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1 | 4.24 | 4.16 | 6.40 | 1.000 | 9 | 9 | 111 | ML |
2 | 6.15 | 6.41 | 8.82 | 1.000 | 6 | 10 | 51 | ML |
3 | 7.10 | 7.63 | 7.79 | 1.000 | 3 | 7 | 46 | ML |
4 | 5.71 | 6.07 | 6.40 | 1.000 | 7 | 7 | 31 | ML |
5 | 5.54 | 5.03 | 6.75 | 1.000 | 8 | 8 | 38 | ML |
6 | 5.63 | 5.37 | 7.09 | 1.000 | 4 | 10 | 70 | ML |
7 | 6.23 | 6.24 | 7.44 | 1.000 | 4 | 10 | 61 | ML |
8 | 5.80 | 5.37 | 7.79 | 1.000 | 7 | 7 | 13 | ML |
9 | 5.71 | 5.89 | 7.44 | 1.000 | 5 | 5 | 12 | ML |
10 | 4.42 | 5.03 | 6.40 | 1.000 | 7 | 11 | 309 | ML |
Ave. | 5.65 (0.79 ) | 5.72 (0.90 ) | 7.23 (0.75 ) | 1.00 (0.00 ) | 6.00 (1.84 ) | 8.40 (1.80 ) | 74.20 (82.95 ) | ML |
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Total | |||||||
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Ground Truth |
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80 | 0 | 1 | 0 | 2 | 0 | 0 | 83 |
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0 | 82 | 0 | 1 | 0 | 0 | 0 | 83 | |
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0 | 0 | 67 | 1 | 14 | 0 | 0 | 82 | |
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1 | 0 | 0 | 79 | 2 | 0 | 0 | 82 | |
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1 | 0 | 8 | 3 | 71 | 0 | 0 | 83 | |
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0 | 0 | 0 | 2 | 0 | 81 | 0 | 83 | |
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0 | 0 | 1 | 0 | 0 | 0 | 81 | 82 | |
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82 | 82 | 77 | 86 | 89 | 81 | 81 | 578 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.095 |
Add-Feature Mutation | 0.089 |
Hoist Mutation | 0.094 |
Truncate Mutation | 0.104 |
Swap Mutation | 0.102 |
One-Symbol Mutation | 0.097 |
All-Nodes Mutation | 0.099 |
One-Node Mutation | 0.101 |
Grow Mutation | 0.103 |
High-Level Crossover | 0.116 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 6 |
Max. Generations | 4 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
segment_3_gen.m | Matlab plot generation function |
segment_3_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
segment_3_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
segment_3_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
segment_3_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
segment_3_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
segment_3_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
segment_3_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
segment_3_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
segment_3_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
segment_3_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
segment_3_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
segment_3_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
segment_3_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
segment_3_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_3_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
segment_3_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_3_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
segment_3_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
segment_3_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_3_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_3_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_3_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_3_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_3_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |