An advance ensemble classification for object recognition

dc.contributor.authorOwusu, E.
dc.contributor.authorWiafe, I.
dc.date.accessioned2021-11-12T09:58:06Z
dc.date.available2021-11-12T09:58:06Z
dc.date.issued2021
dc.descriptionResearch Articleen_US
dc.description.abstractThe quest to improve performance accuracy and prediction speed in machine learning algorithms cannot be overemphasized, as the need for machines to outperform humans continue to grow. Accordingly, several studies have proposed methods to improve prediction performance and speed particularly for spatio-temporal analysis. This study proposes a novel classifier that leverages ensemble techniques to improve prediction performance and speed. The proposed classifier, Ada-AdaSVM uses an AdaBoost feature selection algorithm to select small features of input datasets for a joint support vector machine (SVM)–AdaBoost classifier. The proposition is evaluated against a selection of existing classifiers (SVM, AdaSVM and AdaBoost) using the Jaffe, Yale, Taiwanese facial expression database (TFEID) and CK + 48 datasets with Haar features as the preferred method for feature extraction. The findings indicated that Ada-AdaSVM outperforms SVM, AdaSVM and AdaBoost classifiers in terms of speed and accuracy.en_US
dc.identifier.otherhttps://doi.org/10.1007/s00521-021-05881-3
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/37037
dc.language.isoenen_US
dc.publisherNeural Computing and Applicationsen_US
dc.subjectSVMen_US
dc.subjectAdaboosten_US
dc.subjectAdaSVMen_US
dc.subjectAda-AdaSVMen_US
dc.titleAn advance ensemble classification for object recognitionen_US
dc.typeArticleen_US

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