In our experiments four different agents were involved. Two of them used an ID3-like algorithm (TreeL), and the other two an incremental rule learning algorithm (IncRuleL) [Torgo,1991]. Notice again that all agents use different examples. The purpose of our experiments was to compare the performance of the integrated theory with the performance of the individual theories obtained by each agent. The tests were performed on lymphography data obtained from JSI, Ljubljana. This data set contains 148 examples which are characterized by 18 attributes and there are 4 possible concepts to which each example can belong.
Each theory was generated by an inductive system (TreeL or IncRuleL) on the basis of a given number of examples which were selected from a given pool by a random process. The numbers of training examples used were 5, 10 ,15 ... 50. In order to exclude the possibility of fortuitous results the experiments related to N training examples were repeated 20 times and the mean value of these repetitions obtained.
Figure 2 presents two graphs showing the results obtained on those experiments. Figure 2a compares the performance of the IT with the mean performance of the four agents. On figure 2b we compare the number of rules (complexity) of the IT with the sum of the rules of all agents (giving an idea of how many rules were forgotten).
Fig. 2(b)- Complexity Comparison.