Immunology as a Metaphor for Computational ... - Napier University

Chapter 6. A Self-Organising SDM — SOSDM 165Size of subset 1 Size of subset 2 P(means different) Mean Subset 1 Mean Subset 210 20 0.970369 46.193636 45.45090910 90 0.960757 46.193636 45.53909110 100 0.993885 46.193636 45.29727310 110 0.991606 46.193636 45.34090910 120 0.951153 46.193636 45.57000010 140 0.975882 46.193636 45.45818210 180 0.967632 46.193636 45.49818210 200 0.993710 46.193636 45.33363650 100 0.959255 45.926364 45.29727350 200 0.955828 45.926364 45.33363670 100 0.973447 46.004545 45.29727370 110 0.964623 46.004545 45.34090970 200 0.971447 46.004545 45.333636Table 6.4: The table shows the comparisons of subset size used in FPS which produceda statistically significant difference in mean fitness for the smallest cluster6.6.2.1.2 Average recall of smallest cluster Examining the mean recall of thesmaller group of schemas however shows the opposite trend; for all values of s thebest result found is better than both standard SOSDM and CE-POTTER. The differencesare statistically significant in each case (see table B.5 in appendix B). Thus,there is a trade-off between improving fitness of small groups vs fitness of entire dataset. There is no obvious trend in the results as s increases. In comparisons of meanrecalled accuracy of the smallest cluster for a subset size of size s 1 to a subset of sizes 2 , then in only 13/190 cases are there significant differences observed in the recalledvalues. The cases where statistical differences were observed are summarised in table6.4.6.6.3 Performance of SOSDM vs Size of DatasetExperiments were performed using datasets ranging in size from 500 antigen to 10,000antigen in steps of 500. All datasets were generated using 4 quarter-schema, and themean recalled accuracy of the entire dataset measured at the end of 200 iterations of