Evolving Fuzzy Detectives: An Investigation into the Evolution of Fuzzy Rules

This paper concludes that many factors affect accuracy of classification, intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions, and noise can cause loss of accuracy proportionate to the square of noise.

Abstract

This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise. 1