论文标题
遇见面具：一种新颖的多分类员的验证方法
Meet MASKS: A novel Multi-Classifier's verification approach
论文作者
论文摘要
在这项研究中，引入了一种新的分类器合奏方法。通过集成多个分类器，开发了一种更好地消除误差的验证方法。由多个分类器组成的多机构系统旨在验证安全性能的满意度。为了检查有关分布式知识汇总的推理，已经提出了逻辑模型。为了验证预定义的属性，已经制定并开发了多代理系统的知识共享算法（蒙版）。作为一项严格的评估，我们将此模型应用于时尚，MNIST和FRUIT-360数据集，在此将错误率降低到大约十分之一的单个分类器。
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.
