LEARNING CLASSIFICATION RULES FROM AN ION CHROMATOGRAPHY DATABASE USING A GENETIC BASED CLASSIFIER SYSTEM

A classifier system based on genetic algorithm methodology was develop ed for the automatic extraction of production rules from a database of about 6000 ion chromatography (IC) method examples. This machine lear ning strategy generated heuristics that can assist in the choice for a detection method for a specified set of IC method and solute properti es. It was shown that the final set of rules proposed detectors that a greed with the database for 76% of the cases. Application to a separat e test set showed a prediction ability of 82%. The database, because o f the characteristics of the included cases, did not allow for a signi ficant improvement of these results. However, the results are of signi ficance for the further development of knowledge systems, which assist in the design of IC methods. Furthermore, this dataset comprised a co nsiderable challenge to the applied machine learning method.