关键词: 直推式学习, 多主体系统, 自组织, 多分类

Abstract: Aiming at the problem of multiclass classification in which both a few labeled data and lots of unlabeled data were given, a novel approach called Multi-Agent-System-Based Multi-Class Transductive Learning was presented. All the data objects were carried by agents and then mapped to the output space, and the spatial configuration of the agents formed a self-organizing Markov stochastic process. The Markov process finally converged to a stationary probability distribution, in which an optimal label distribution was obtained. Based on the proposed approach, two MMTA (Multi-Agent-System-Based Multi-Class Transductive Algorithms) algorithms were put forward and their convergence as well as time complexities were discussed. The simulations were provided to demonstrate the effectiveness and practicability of MMTA algorithms.

Key words: transductive learning, multi-agent-system, self-organization, multi-class