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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