HIS'2008: Genetic-based Synthetic Data Sets for the Analysis of Classifiers Behavior

This document presents a genetic algorithm approach to generating synthetic data sets for analyzing classifier behavior. The genetic algorithm represents data set labelings as binary strings and uses genetic operators like crossover and mutation to evolve solutions that satisfy the desired complexity based on class boundary length. Experiments show the genetic algorithm can generate intermediate complexity data sets in early generations and produce similar accuracy rates across different classifier paradigms, while allowing control over the data set properties. Future work aims to improve efficiency and scalability, enable multiple criteria optimization, and develop benchmark problems with more realistic structure.