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.