基于Multi-Agent的分类器融合

针对决策层输出的分类器融合问题，该文提出了一种基于Multi-Agent思想的融合算法，该算法将分类器融合问题建模为人类发源地问题，通过引入决策共现矩阵，并在智能体之间进行信息交互，从而利用了分类器之间的决策相关信息，算法根据在融合训练集上得到的统计参量，指导各个智能体向不同类别溯源，并通过智能体之间的信息交换改变溯源概率，最终达到群体决策，得到决策类别，本文在标准数据集上对该算法进行了实验研究，通过与其它一些融合方法的比较，得出在用于融合的分类器较少时，该算法得到比其它方法更低的分类错误率，其空间复杂度相对BKS方法较小，实验证实，该算法是收敛的。

Multi-Agent Based Classifier Combination

In this paper, a classifier combination algorithm based on multi-agent system is presented at abstract level. The problem is modeled as people's tracing back to their homeland: once upon a time, people from a region left their homeland and resided in different places, decades later, their offspring set out to find their homeland according to the wide spread legends of the ancestors' origin. In this combination problem, the class label of a testing sample serves as the homeland, decisions made by classifiers serve as offspring's residual places, and legends are class creditability by classifiers acquired from combination training set. Messengers sent by offspring try to trace back to their homeland according to the legends. They act as agents and exchange information with one another, so that confidences of different places being the original place change gradually. After congruence among the messengers is achieved, combination decision is made. The co-decision matrix is used for information exchange between agents, thus relativity between classifiers is utilized, while it is rarely considered in Bayesian Rule. According to experiments on standard database, when the number of classifiers used in combination is small, this algorithm lead to less error than other methods, and its space complexity is lower than Behavior Knowledge Space (BKS) method. Experiments show that the algorithm is convergent.