【摘要】 论文提出了一种基于专家域的多层分类器融合模型,专家指不同专长之单分类器。模型思想来自医院诊断流程,模型首先训练n个专家,之后将样本空间按专家专长划分专家域。对于待测样本,先将样本指派到合适的专家域,然后再由指定的专家对样本进行分类。用这种算法对UCI的标准数据集进行分类,实验结果显示,该算法得到比其他算法更低的分类误差,显著提高了分类器的性能。更多还原

【Abstract】 In this paper,a new model of multiple layer classifiers integration based on specialists’ fields is introduced,and specialists are the classifiers with different algorithm.The idea of model is derived from diagnosing flow in hospital.At first,n methods are adopted to train single classifier and gain n classifiers,and every classifier is called as specialist.Then using the training set to test every specialist,we gain n specialists’ fields according to the result of classification of every specialist.For an unknown sample,we assign it to specialist’s field which it belongs to,and select the specialist on that field to classify this sample.We use UCI standard datasets to test our model,according to experiments our algorithm leads to less error and better performance than other algorithms.更多还原