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All of these solutions are compared to the standard Adaboost on thirteen Gunnar Raetsch\u2019s datasets under three levels of class-label noise. To test the proposed method on a real application, electroencephalography (EEG) signals of 20 schizophrenic patients and 20 age-matched control subjects, are recorded via 20 channels in the idle state. Several features including autoregressive coefficients, band power and fractal dimension are extracted from EEG signals of all participants. Sequential feature subset selection technique is adopted to select the discriminative EEG features. Experimental results imply that exploiting the proposed hunting techniques enhance the Adaboost performance as well as alleviating its robustness against unconfident and noisy samples over Raetsch benchmark and EEG features of the two groups.<\/jats:p>","DOI":"10.3233\/ida-227125","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T16:31:48Z","timestamp":1702398708000},"page":"357-376","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Adaboost performance in the presence of class-label noise: A comparative study on EEG-based classification of schizophrenic patients and benchmark datasets"],"prefix":"10.1177","volume":"28","author":[{"given":"Omid Ranjbar","family":"Pouya","sequence":"first","affiliation":[{"name":"Independent Researcher, Burnaby, Canada"}]},{"given":"Reza","family":"Boostani","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Shiraz University, Fars, Iran"}]},{"given":"Malihe","family":"Sabeti","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Islamic Azad University, Tehran, Iran"}]}],"member":"179","reference":[{"issue":"9","key":"10.3233\/IDA-227125_ref1","doi-asserted-by":"crossref","first-page":"3408","DOI":"10.1109\/JBHI.2021.3068481","article-title":"A combinatorial deep learning structure for precise depth of anesthesia estimation from EEG signals","volume":"25","author":"Afshar","year":"2021","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.3233\/IDA-227125_ref2","doi-asserted-by":"crossref","unstructured":"F. 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Moussavi, Comparison of a new ad-hoc classification method with the ensemble classifiers for the diagnosis of Meniere\u2019s disease using EVestG signals, in: 29\u2019th IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, Canada, 2016.","DOI":"10.1109\/CCECE.2016.7726799"},{"issue":"1","key":"10.3233\/IDA-227125_ref13","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.eswa.2010.07.078","article-title":"Boosting a multi-linear classifier with application to visual lip reading","volume":"38","author":"Deypir","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-227125_ref14","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neucom.2013.03.044","article-title":"A general framework to estimate spatial and spatio-spectral filters for EEG signal classification","volume":"119","author":"Fattahi","year":"2013","journal-title":"Neurocomputing"},{"issue":"2","key":"10.3233\/IDA-227125_ref17","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting with discussions","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann Stat"},{"issue":"1\u20132","key":"10.3233\/IDA-227125_ref20","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/0004-3702(94)00094-8","article-title":"Noise modelling and evaluating learning from examples","volume":"82","author":"Hickey","year":"1996","journal-title":"Artif. 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