{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:00:44Z","timestamp":1779249644826,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology Research","award":["MOST 110-2410-H-A49-014-"],"award-info":[{"award-number":["MOST 110-2410-H-A49-014-"]}]},{"name":"Ministry of Science and Technology Research","award":["202102AA100021"],"award-info":[{"award-number":["202102AA100021"]}]},{"name":"Ministry of Science and Technology Research","award":["202101AT070167"],"award-info":[{"award-number":["202101AT070167"]}]},{"name":"Ministry of Science and Technology Research","award":["2020SE311"],"award-info":[{"award-number":["2020SE311"]}]},{"name":"Key Research and Development Program of Yunnan Province","award":["MOST 110-2410-H-A49-014-"],"award-info":[{"award-number":["MOST 110-2410-H-A49-014-"]}]},{"name":"Key Research and Development Program of Yunnan Province","award":["202102AA100021"],"award-info":[{"award-number":["202102AA100021"]}]},{"name":"Key Research and Development Program of Yunnan Province","award":["202101AT070167"],"award-info":[{"award-number":["202101AT070167"]}]},{"name":"Key Research and Development Program of Yunnan Province","award":["2020SE311"],"award-info":[{"award-number":["2020SE311"]}]},{"name":"Yunnan Natural Science Foundation Project","award":["MOST 110-2410-H-A49-014-"],"award-info":[{"award-number":["MOST 110-2410-H-A49-014-"]}]},{"name":"Yunnan Natural Science Foundation Project","award":["202102AA100021"],"award-info":[{"award-number":["202102AA100021"]}]},{"name":"Yunnan Natural Science Foundation Project","award":["202101AT070167"],"award-info":[{"award-number":["202101AT070167"]}]},{"name":"Yunnan Natural Science Foundation Project","award":["2020SE311"],"award-info":[{"award-number":["2020SE311"]}]},{"name":"Open Foundation of Key Laboratory in Software Engineering of Yunnan Province","award":["MOST 110-2410-H-A49-014-"],"award-info":[{"award-number":["MOST 110-2410-H-A49-014-"]}]},{"name":"Open Foundation of Key Laboratory in Software Engineering of Yunnan Province","award":["202102AA100021"],"award-info":[{"award-number":["202102AA100021"]}]},{"name":"Open Foundation of Key Laboratory in Software Engineering of Yunnan Province","award":["202101AT070167"],"award-info":[{"award-number":["202101AT070167"]}]},{"name":"Open Foundation of Key Laboratory in Software Engineering of Yunnan Province","award":["2020SE311"],"award-info":[{"award-number":["2020SE311"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring. To address this concern, the data rotational method by PCA in tree-based methods shows a path. This work tries to enhance this path by proposing an ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost. The random rotation process has replaced the manual pre-defined number of subset features (free pre-defined process). Therefore, with the ensemble of the multiple AdaBoost-based classifier, overfitting problems can be avoided, thus reinforcing the robustness. In our experiments with real-world medical data sets, Random RotBoost reaches better classification performance when compared with existing methods. Thus, with the help from our proposed method, the quality of clinical decisions can potentially be enhanced and supported in medical tasks.<\/jats:p>","DOI":"10.3390\/e24050617","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T22:20:06Z","timestamp":1651184406000},"page":"617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support"],"prefix":"10.3390","volume":"24","author":[{"given":"Shin-Jye","family":"Lee","sequence":"first","affiliation":[{"name":"Institute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Hsun","family":"Tseng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui-Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2211-2006","authenticated-orcid":false,"given":"Xin","family":"Jin","sequence":"additional","affiliation":[{"name":"National Pilot School of Software, Yunnan University, Kunming 650504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Jiang","sequence":"additional","affiliation":[{"name":"National Pilot School of Software, Yunnan University, Kunming 650504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Pu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Huan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duen-Ren","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Information Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Pilot School of Software, Yunnan University, Kunming 650504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","first-page":"81","article-title":"Artificial neural networks","volume":"45","author":"Mitchell","year":"1997","journal-title":"Mach. 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