让多个能力有限的分类器通过协作，逐步学会识别真实类别。
Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers
- 每个智能体基于本地数据和邻居信念，递归更新自身判断。
- 算法保证所有智能体对真实类别的信念几乎必然收敛到1。
- 适合分布式场景中弱分类器协同学习，如边缘计算或传感器网络。
我们研究在异构且信息不完整的代理网络中进行分类的问题，每个代理接收由真实类别生成的局部数据，并配备只能区分部分类别的分类器。提出一种迭代算法，利用本地分类器的后验概率，结合邻居的信念信息，递归更新每个代理对所有可能类别的局部信念。随后采用新型分布式最小规则更新各代理的全局信念，实现对真实类别的学习。在特定假设下，证明所有代理对真实类别的信念几乎必然渐近收敛至1，并给出收敛速率。通过图像数据仿真验证算法性能，实验使用随机森林分类器和MobileNet模型。
原文摘要 · Abstract (English)
We consider the problem of classification with a (peer-to-peer) network of heterogeneous and partially informative agents, each receiving local data generated by an underlying true class, and equipped with a classifier that can only distinguish between a subset of the entire set of classes. We propose an iterative algorithm that uses the posterior probabilities of the local classifier and recursively updates each agent's local belief on all the possible classes, based on its local signals and belief information from its neighbors. We then adopt a novel distributed min-rule to update each agent's global belief and enable learning of the true class for all agents. We show that under certain assumptions, the beliefs on the true class converge to one asymptotically almost surely. We provide the asymptotic convergence rate, and demonstrate the performance of our algorithm through simulation with image data and experimented with random forest classifiers and MobileNet.
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