The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance

The learning classifier system (LCS) integrates a rule-based system with re inforcement learning and genetic algorithm-based rule discovery. This inves tigation reports on the design, implementation, and evaluation of EpiCS, a LCS adapted for knowledge discovery in epidemiologic surveillance. Using da ta from a large, national child automobile passenger protection program, Ep iCS was compared with C4.5 and logistic regression to evaluate its ability to induce rules from data that could be used to classify cases and to deriv e estimates of outcome risk, respectively. The rules induced by EpiCS were less parsimonious than those induced by C4.5, but were potentially more use ful to investigators in hypothesis generation. Classification performance o f C4.5 was superior to that of EpiCS (P < 0.05). However, risk estimates de rived by EpiCS were significantly more accurate than those derived by logis tic regression (P < 0.05). (C) 2000 Elsevier Science B.V. All rights reserv ed.