RULE GENERATION AND SELECTION WITH A PARALLEL GENERALIZATION ARCHITECTURE

The BAMBOO algorithm is an expert system rule generating algorithm developed from the well-known C4 decision tree algorithm. Because BAMBOO's search is less restricted than C4's it usually finds simpler rules than C4. Both algorithms have problems with incomplete search and brittleness. These problems can be avoided by layering both algorithms together with other algorithms, generating independent rule sets and selecting a subset of rules to use in the final expert system. This learning strategy is referred to as parallel generalisation. Problems of search and brittleness are because the algorithms have a single fixed bias. By layering several algorithms together the effect is of a single algorithm selectively applying many heuristics. Because selecting rules is much easier than generating rules, the select procedure has its own parameterised bias. The layered algorithm is much more flexible than the single algorithms, in addition to generating more accurate and concise rule sets. Brittleness is avoided as when one algorithm produces a worst case rule set other algorithms generate better rules. Parallel generalisation can be improved by altering the algorithms to cooperate more.