{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T09:50:29Z","timestamp":1769161829658,"version":"3.49.0"},"reference-count":23,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,4,18]]},"abstract":"<jats:p>Ensemble learners and deep neural networks are state-of-the-art schemes for classification applications. However, deep networks suffer from complex structure, need large amount of samples and also require plenty of time to be converged. In contrast, ensemble learners (especially AdaBoost) are fast to be trained, can work with small and large datasets and also benefit strong mathematical background. In this paper, we have developed a new orthogonal version of AdaBoost, termed as ORBoost, in order to desensitize its performance against noisy samples as well as exploiting low number of weak learners. In ORBoost, after reweighting the distribution of each learner, the Gram-Schmidt rule updates those weights to make a new samples\u2019 distribution to be orthogonal to the former distributions. In contrast in AdaBoost, there is no orthogonality constraint even between two successive weak learners while there is a similarity between the distributions of samples in different learners. To assess the performance of ORBoost, 16 UCI-Repository datasets along with six big datasets are deployed. The performance of ORBoost is compared to the standard AdaBoost, LogitBoost and AveBoost-II over the selected datasets. The achieved results support the significant superiority of ORBoost to the counterparts in terms of accuracy, robustness, number of exploited weak learners and generalization on most of the datasets.<\/jats:p>","DOI":"10.3233\/ida-205705","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T17:24:33Z","timestamp":1650993873000},"page":"805-818","source":"Crossref","is-referenced-by-count":5,"title":["ORBoost: An Orthogonal AdaBoost"],"prefix":"10.1177","volume":"26","author":[{"given":"Zohreh","family":"Bostanian","sequence":"first","affiliation":[{"name":"CSE & IT Department, Electrical and Computer Engineering Faculty, Shiraz University, Shiraz, Iran"}]},{"given":"Reza","family":"Boostani","sequence":"additional","affiliation":[{"name":"CSE & IT Department, Electrical and Computer Engineering Faculty, Shiraz University, Shiraz, Iran"}]},{"given":"Malihe","family":"Sabeti","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran"}]},{"given":"Mokhtar","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, Iraq"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205705_ref2","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"The Annals of Statistics"},{"key":"10.3233\/IDA-205705_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1748-0221\/8\/12\/P12013","article-title":"UBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers","volume":"8","author":"Stevens","year":"2013","journal-title":"Journal of Instrumentation"},{"key":"10.3233\/IDA-205705_ref7","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization","volume":"40","author":"Dietterich","year":"2000","journal-title":"Machine Learning"},{"key":"10.3233\/IDA-205705_ref8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3233\/IDA-150357","article-title":"A novel density-based ensemble learning algorithm with application to protein structural classification","volume":"21","author":"Homayouni","year":"2017","journal-title":"Intelligent Data Analysis (IDA)"},{"key":"10.3233\/IDA-205705_ref9","doi-asserted-by":"crossref","unstructured":"M. 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