A stable feature selection approach for optimizing traffic classification based on adaptive threshold

作者：Duan Wenbei^*; Wang Yuanli

来源：2nd International Conference on Electronics, Network and Computer Engineering (ICENCE), 2016-08-13 to 2016-08-14.

• Traffic classification
• Feature selection
• ATFS
• TRF
• WSU

In recent years, machine learning algorithm has been widely studied in the field of traffic classification. However, most studies focus on performance improvement of classifier, pro-phase work of traffic classification - feature selection is ignored. Therefore, WSU is regarded as metric, an ATFS algorithm - (Adaptive threshold feature select) is designed on the basis. Namely, algorithm is based on precision autonomous selection threshold of classifier aiming at different datasets. Each dataset will generate a set of attribute subset eventually. Stable features are selected in different screened attribute subsets through TRF algorithm, thereby reaching the purpose of high precision. The experiment shows that the features finally selected in the algorithm can reach the precision of > 96% on C4.5 classifier.

• 出版日期2016
• 单位武汉理工大学