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

TitleThe learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance.
Publication TypeJournal Article
Year of Publication2000
AuthorsHolmes JH, Durbin D, Winston FK
JournalArtif Intell Med
Volume19
Issue1
Pagination53-74
Date Published2000 May
ISSN0933-3657
KeywordsAlgorithms, Artificial Intelligence, Biological Evolution, Child, Classification, Epidemiologic Methods, Humans, Infant Equipment, Logistic Models, Population, Risk Assessment
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).

Alternate JournalArtif Intell Med
PubMed ID10767616