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arXiv · 2007.10090

Meet MASKS: A novel Multi-Classifier's verification approach

Amirhoshang Hoseinpour Dehkordi·Majid Alizadeh·Ali Movaghar
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Abstract

In this study, a new ensemble approach for classifiers is introduced. A verification method for better error elimination is developed through the integration of multiple classifiers. A multi-agent system comprised of multiple classifiers is designed to verify the satisfaction of the safety property. In order to examine the reasoning concerning the aggregation of the distributed knowledge, a logical model has been proposed. To verify predefined properties, a Multi-Agent Systems' Knowledge-Sharing algorithm (MASKS) has been formulated and developed. As a rigorous evaluation, we applied this model to the Fashion-MNIST, MNIST, and Fruit-360 datasets, where it reduced the error rate to approximately one-tenth of the individual classifiers.

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Amirhoshang Hoseinpour Dehkordi, Majid Alizadeh, Ali Movaghar. 2020-07-03. Meet MASKS: A novel Multi-Classifier's verification approach. https://arxiv.org/abs/2007.10090

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