Adapting to concept drift with genetic programming for classifying streaming data
Murray Smith, Vic Ciesielski · 2016
Concept drift in data streams is a change in the underlying distribution that can cause algorithms that are classifying them to have increased error. There is a need for algorithms that can adapt to these changes. Genetic programming is one such algorithm that can adapt to streaming data however its use in this area is somewhat unexplored. It is hoped that because genetic programming is a population based method the variety of solutions tested every generation will enable it to adapt quickly. The adaptation speed can be increased by determining suitable parameters and settings. In order to test these ideas, experiments were run on several synthetic and one world streaming data set. The results found that genetic programming was capable of adapting quickly to concept drift and that increased rates of mutation and crossover can provide faster adaptation.