关键词: 选择集成, 异常数据流, 决策轮廓矩阵, 支持熵, 差异度量

Abstract: Traditional classifier selection algorithm generates a large computing and storage overhead. Another, for the forecast stability of abnormal data flow, multiple classifiers is an important factor to solve the concept drift. This paper has solved the problem about fuzzy degree of difference between each classifier collection by introducing the improved decision contour matrix and the support entropy. The degree of differences uses support entropy as standard of input measure, making calculation of differences in each classifier collection more stable and efficient. An abnormaly data flow detection method and algorithm based on diversity integration is proposed. The algorithm is applied to the anomaly classifier selection module, and mainly includes three processes：constructing decision contour matrix, integrating support entropy and measuring classifier ensemble dissimilarity. Experimental result shows that both accuracy and stability of the BDMS algorithm are better than other algorithms in accuracy and stability of abnormal traffic prediction. Since the classifier training time reach about 10－2 s, basically it is able to adapt to the real-time demand for data traffic.

Key words: selection integration, abnormal data flow, decision contour matrix, support entropy, difference measure