Diversity, accuracy and efficiency in ensemble learning: An unexpected result
Joseph Johnson, Christophe G. Giraud-Carrier · Intelligent Data Analysis · 2019
Empirical evidence suggests that ensembles with adequate levels of pairwise diversity among a set of accurate member algorithms can significantly outperform any of the individual algorithms. As a result, several diversity measures have been developed for use in optimizing ensembles. We show, howeve r, that there is natural tension between the pairwise diversity of ensemble members and their individual accuracy. While efficient ensembles can be built with stronger forms of diversity, they also suffer in overall accuracy. On the other hand, ensembles built with weaker forms of diversity can be very accurate, but tend to be significantly more computationally expensive. We discuss these findings in light of the notion of diversity space.