一种基于频繁子图的集成分类算法

An Ensemble Classification Algorithm Based on Frequent Subgraphs

针对基于频繁子图的图分类算法不能有效解决高效和分类正确率并存的矛盾,提出G-Bagging图分类算法。该算法利用传统图分类算法训练出多个基图分类器,集成学习加权构造集成分类器,余度管理实时更新权值。通过实验,表明G-Bagging算法降低了对最小支持度和训练样本空间大小的要求,即在算法效率提高的同时,保证了分类正确率。

Aiming at the contradiction of efficiency and correct rate existing in graph classification based on frequent subgraphs,the paper comes up with an algorithm for graph classification named G-Bagging. The algorithm makes base classifiers by traditional algorithm,and makes ensemble classifier by ensemble learning base classifiers,and updates ensemble classifier by redundancy management. Then we demonstrate that the algorithm can reduce the requirement of minimum support and training samples space by experiment,also is that the algorithm can ensure both efficiency and correct rate.