Learning classification rules from an ion chromatography database using a genetic based classifier system

A classifier system based on genetic algorithm methodology was developed for the automatic extraction of production rules from a database of about 6000 ion chromatography (IC) method examples. This machine learning strategy generated heuristics that can assist in the choice for a detection method for a specified set of IC method and solute properties. It was shown that the final set of rules proposed detectors that agreed with the database for 76% of the cases. Application to a separate test set showed a prediction ability of 82%. The database, because of the characteristics of the included cases, did not allow for a significant improvement of these results. However, the results are of significance for the further development of knowledge systems, which assist in the design of IC methods. Furthermore, this dataset comprised a considerable challenge to the applied machine learning method.