SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification
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DOI: 10.1109/access.2019.2933262
Abstract: In the past decades, the ensemble systems have been shown as an efficient method to increase the accuracy and stability of classification algorithms. However, how to get a valid combination of multiple base-classifiers is still… read more here.
Keywords: ssem; self adaptive; adaptive stacking; base classifiers ... See more keywords
Combining Linear Classifiers Using Probability-Based Potential Functions
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DOI: 10.1109/access.2020.3038341
Abstract: The score function can be used as a measure for evaluating predicted probabilities of the classification models. In multiple classifiers systems, one of the problems is the diversity of the way of determining the scoring… read more here.
Keywords: potential functions; base classifiers; based potential; probability ... See more keywords