title: The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance.
abstract: The learning classifier system (LCS) integrates a rule-based system with reinforcement learning and genetic algorithm-based rule discovery. This investigation reports on the design, implementation, and evaluation of EpiCS, a LCS adapted for knowledge discovery in epidemiologic surveillance. Using data from a large, national child automobile passenger protection program, EpiCS 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 derive estimates of outcome risk, respectively. The rules induced by EpiCS were less parsimonious than those induced by C4.5, but were potentially more useful to investigators in hypothesis generation. Classification performance of C4.5 was superior to that of EpiCS (P<0.05). However, risk estimates derived by EpiCS were significantly more accurate than those derived by logistic regression (P<0.05).
authors.affiliation: Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 106 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, USA. jholmes@cceb.med.upenn.edu
authors.forename: J H
authors.initials: JH
authors.lastname: Holmes
authors.forename: D R
authors.initials: DR
authors.lastname: Durbin
authors.forename: F K
authors.initials: FK
authors.lastname: Winston
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D002648:Child; D002965:Classification; D004812:Epidemiologic Methods; D006801:Humans; D015370:Infant Equipment; D016015:Logistic Models; D011153:Population; D018570:Risk Assessment