Evolutionary learning of meta-rules for text classification.

This paper presents an evolutionary method for learning lists of meta-rules for generalizing the selection of the best classifier for a given text dataset. The method builds rules based on features of a set of training text datasets, and evolves them using special crossover and mutation operators. Once the rules are learned, they are tested in a different set of datasets to demonstrate their accuracy and generality. Our experiments show encouraging results.