Search-based framework for transparent non-overlapping ensemble models

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Gulowaty:2022:IJCNN,

• author = "Bogdan Gulowaty and Michał Woźniak",
• booktitle = "2022 International Joint Conference on Neural Networks (IJCNN)",
• title = "Search-based framework for transparent non-overlapping ensemble models",
• year = "2022",
• abstract = "Due to their generalizing ability, classifier ensembles are considered very powerful predictive models. A typical ensemble consists of a static or dynamic pool of classifiers and a combination method, which translates predictions of many models into one. The combination step is often complex and renders the inner behavior of the whole ensemble incomprehensible to a typical user. In this work, in the light of recent interest in Explainable AI (XAI) research, we are proposing a novel approach to building an interpretable ensemble model. It is based on decision space splitting into non-overlapping regions. Every area has an assigned interpretable classifier and its boundaries are selected using the genetic programming approach. We experimentally evaluate the proposed method and compare it to Decision Tree and Random Forest. The results show that the proposed approach is competitive with the state-of-the-art techniques and prone to further expansion.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IJCNN55064.2022.9892360",
• ISSN = "2161-4407",
• month = jul,
• notes = "Also known as \cite{9892360}",

}

Genetic Programming entries for Bogdan Gulowaty Michał Woźniak

Citations