Abstrakt
Metalearning -- the recommendation of a suitable machine learning technique for a given dataset -- relies on the concept of similarity between datasets. Traditionally, similarity measures have been constructed manually, and thus could not precisely grasp the complex relationship among the different features of the datasets.
Recently, we have used an attribute alignment technique combined with genetic programming to obtain more fine-grained and trainable dataset similarity measure. In this paper, we propose an approach based on multi-objective genetic programming for evolving an attribute similarity function.
Multi-objective optimization is used to encourage some of the metric properties, thus contributing to the generalization abilities of the similarity function being evolved. Experiments are performed on the data extracted from the OpenML repository and their results are compared to the baseline algorithm.