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An advance ensemble classification for object recognition

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Abstract

The 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.

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Authors and Affiliations

  1. Department of Computer Science, University of Ghana, Accra, Ghana

    Ebenezer Owusu & Isaac Wiafe

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  1. Ebenezer Owusu
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  2. Isaac Wiafe
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Correspondence to Isaac Wiafe.

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Conflict of interest and authorship confirmation form please check the following as appropriate: all authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

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Owusu, E., Wiafe, I. An advance ensemble classification for object recognition. Neural Comput & Applic 33, 11661–11672 (2021). https://doi.org/10.1007/s00521-021-05881-3

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