@inproceedings { , title = {A homogeneous-heterogeneous ensemble of classifiers.}, abstract = {In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module.}, conference = {27th International conference on neural information processing 2020 (ICONIP 2020)}, doi = {10.1007/978-3-030-63823-8\_30}, isbn = {9783030638221}, note = {INFO COMPLETE (Info via Scopus alert 10/12/2020 LM) PERMISSION GRANTED (version = AAM; embargo = none; licence = Publisher's own; POLICY= https://www.springer.com/gp/open-access/publication-policies/self-archiving-policy 11/12/2020 LM) PENDING DOCUMENT (AAM requested from contact 11/12/2020 LM) ADDITIONAL INFO: Thanh Nguyen; John McCall The final authenticated version is available online at https://doi.org/10.1007/978-3-030-63823-8\_53 . © SpringerNature – Terms of Reuse detailed at https://www.springer.com/gp/open-access/publicationpolicies/aam-terms-of-use}, pages = {251-259}, publicationstatus = {Published}, publisher = {Springer}, url = {https://rgu-repository.worktribe.com/output/1005529}, keyword = {Computational Intelligence (CI), Ensemble method, Multiple classifiers, Combining classifiers, Random projection, Ensemble learning, Combining methods}, year = {2020}, author = {Luong, Anh Vu and Vu, Trung Hieu and Nguyen, Phuong Minh and Van Pham, Nang and McCall, John and Liew, Alan Wee-Chung and Nguyen, Tien Thanh} editor = {Yang, Haiqin and Pasupa, Kitsuchart and Leung, Andrew Chi-Sing and Kwok, James T. and King, Irwin and Chan, Jonathan H.} }