Formentín Garcia, NathanTiggeman, FredericoBorges, Eduardo N.Lucca, GiancarloSantos, HelidaPereira Dimuro, Graçaliz2022-09-212022-09-212021Garcia, N.; Tiggeman, F.; Borges, E.; Lucca, G.; Santos, H. and Dimuro, G. (2021). Exploring the Relationships between Data Complexity and Classification Diversity in Ensembles. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8; ISSN 2184-4992, pages 652-659. DOI: 10.5220/001044000652065910.5220/0010440006520659https://academica-e.unavarra.es/handle/2454/44108Several classification techniques have been proposed in the last years. Each approach is best suited for a particular classification problem, i.e., a classification algorithm may not effectively or efficiently recognize some patterns in complex data. Selecting the best-tuned solution may be prohibitive. Methods for combining classifiers have also been proposed aiming at improving the generalization ability and classification results. In this paper, we analyze geometrical features of the data class distribution and the diversity of the base classifiers to understand better the performance of an ensemble approach based on stacking. The experimental evaluation was conducted using 32 real datasets, twelve data complexity measures, five diversity measures, and five heterogeneous classification algorithms. The results show that stacked generalization outperforms the best individual base classifier when there is a combination of complex and imbalanced data with diverse predictions among weak learners.application/pdfeng© 2021 by SCITEPRESS-Science and Technology Publications, Lda. Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Machine learning ensemblesComplexity measuresDiversity measuresinfo:eu-repo/semantics/conferenceObject2022-09-21info:eu-repo/semantics/openAccess