Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers

TL;DR

This paper introduces a distributed iterative algorithm for classification in networks of heterogeneous agents with partial information, enabling all agents to asymptotically learn the true class through local and neighbor information exchange.

Contribution

The paper presents a novel distributed belief update algorithm that guarantees convergence to the true class in networks of partially informative classifiers, with proven convergence properties.

Findings

Beliefs on the true class converge asymptotically almost surely.

The convergence rate of the belief update algorithm is characterized.

Simulation results demonstrate effective classification with image data using random forests and MobileNet.

Abstract

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.