@article{ba3574a92d9b4830b0304ffd2b799117,
title = "A hierarchical fusion framework to integrate homogeneous and heterogeneous classifiers for medical decision-making",
abstract = "Classifier diversity and fusion architecture are two critical characteristics stressed in homogeneous and heterogeneous ensemble learning methods and they are equally important for building a successful multi-classifier system. In this study, we introduced a two-level framework, namely hierarchical fusion of homogeneous and heterogeneous multi-classifiers (HF2HM), to integrate the diversified classification models produced by feeding heterogeneous classifiers with homogeneous random-projected training datasets. The proposed hierarchical fusion scheme was comprehensively validated using fifteen public UCI datasets and three clinical datasets. The experimental results demonstrated the superiority of the proposed HF2HM framework over the base classifiers and the state-of-the-art benchmark ensemble methods, verifying it as a potential tool to assist in medical decision making in practical clinical settings.",
keywords = "Ensemble diversity, Ensemble method, Fusion architecture, Heterogeneous ensemble, Homogeneous ensemble",
author = "Linjing Wang and Tianlan Mo and Xuetao Wang and Wentao Chen and Qiang He and Xin Li and Shuxu Zhang and Ruimeng Yang and Jialiang Wu and Xuejun Gu and Jun Wei and Peiliang Xie and Linghong Zhou and Xin Zhen",
note = "Funding Information: This work is supported in part by the National Natural Science Foundation of China ( 81874216 & 81971574 ), the Ministry of Education Industry-Academic Cooperation Project ( 201902119002 ), the Guangzhou Key Medical Discipline Construction Project Fund, the Guangzhou Hygiene and Health Scientific Project ( 20191A011102 ), Natural Science Foundation of Guangdong Province in China ( 2018A030313282 ), the Science and Technology Program of Guangzhou in China ( 202002030268 ), and the Chenzhou Science and Technology Project ( jsyf2017030 ). Funding Information: This work is supported in part by the National Natural Science Foundation of China (81874216 & 81971574), the Ministry of Education Industry-Academic Cooperation Project (201902119002), the Guangzhou Key Medical Discipline Construction Project Fund, the Guangzhou Hygiene and Health Scientific Project (20191A011102), Natural Science Foundation of Guangdong Province in China (2018A030313282), the Science and Technology Program of Guangzhou in China (202002030268), and the Chenzhou Science and Technology Project (jsyf2017030). Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",
year = "2021",
month = jan,
day = "5",
doi = "10.1016/j.knosys.2020.106517",
language = "English (US)",
volume = "212",
journal = "Knowledge-Based Systems",
issn = "0950-7051",
publisher = "Elsevier",
}