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

• URL: http://arxiv.org/abs/2007.10090v3
• Date: Thu, 2 Jun 2022 15:26:24 GMT
• Title: Meet MASKS: A novel Multi-Classifier's verification approach
• Authors: Amirhoshang Hoseinpour Dehkordi, Majid Alizadeh, Ali Movaghar
• Abstract summary: Multi-agent system comprised of multiple classifiers is designed to verify the satisfaction of the safety property. A logical model has been proposed to examine the reasoning concerning the aggregation of the distributed knowledge. As a rigorous evaluation, we applied this model to the Fashion-MNIST, MNIST, and Fruit-360 datasets.
• Score: 2.588063924663932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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