Application of hybrid symbolic ensembles to gene expression analyses

This paper considers a class of hybrid (heterogeneous) ensembles purely composed of symbolic elements. In learning diagnostic rules from gene expressions they demonstrate a significant improvement of accuracy with a small number of ensemble elements. This makes them suitable for learning of understandable knowledge, leading to diagnosis and its explanation in original terms (attributes).