Evolutionary learning of selection hyper-heuristics for text classification

This paper introduces an evolutionary model in the scope of automated machine learning. This model is in charge of learning hyper-heuristics that represent selection rules of the form if-then, such that given a dataset for a text classification problem, the hyper-heuristics select the best classification method to use with it, based on the data distribution of the dataset. The evolutionary model starts by building a set of hyper-heuristics using a series of meta-features extracted from a training group of datasets that represent their data distribution. Hyper-heuristics are then evolved using adapted crossover and mutation operators. During the evolution, each hyper-heuristic is evaluated on its performance to classify each dataset in the training group. When the evolutionary process is done, the best hyper-heuristic is selected and evaluated for its generality with an independent test group of datasets. The results show that the best learned hyper-heuristic obtains an average classification performance close to the general optimum, and has a similar performance to the two most popular state-of-the-art automated machine learning systems, but with less computational cost. The approach used by the present model is relevant for automated machine learning in three aspects, the generality of the hyper-heuristics so they could be applied to groups of datasets; the interpretability of the representations that facilitate the understanding of the method selection by non-expert users; and the reduction of computational time and resources to reach a decision. Furthermore, the model extends the applicability of evolutionary computation methods, with their problem-independent properties and their ability to explore search spaces, to tackle new complex problems, such as the decision of the best classifier for a text classification dataset.