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Meet MASKS: A novel Multi-Classifier’s verification approach
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  • Amirhoshang Hoseinpour Dehkordi ,
  • Majid Alizadeh ,
  • Ebrahim Ardeshir-Larijani ,
  • Ali Movaghar
Amirhoshang Hoseinpour Dehkordi
Institute for Research in Fundamental Sciences, Institute for Research in Fundamental Sciences

Corresponding Author:[email protected]

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Majid Alizadeh
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Ebrahim Ardeshir-Larijani
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Ali Movaghar
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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.