Open AccessUniversal Feature Extraction for Traffic Identification of the Target Category
Author(s) -
Jian Shen,
Jingbo Xia,
Shiming Dong,
Xiaoyan Zhang,
Kai Fu
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0165993
Subject(s) - feature extraction , pattern recognition (psychology) , artificial intelligence , universality (dynamical systems) , computer science , identification (biology) , feature (linguistics) , data mining , biology , linguistics , philosophy , botany , physics , quantum mechanics
Traffic identification of the target category is currently a significant challenge for network monitoring and management. To identify the target category with pertinence, a feature extraction algorithm based on the subset with highest proportion is presented in this paper. The method is proposed to be applied to the identification of any category that is assigned as the target one, but not restricted to certain specific category. We divide the process of feature extraction into two stages. In the stage of primary feature extraction, the feature subset is extracted from the dataset which has the highest proportion of the target category. In the stage of secondary feature extraction, the features that can distinguish the target and interfering categories are added to the feature subset. Our theoretical analysis and experimental observations reveal that the proposed algorithm is able to extract fewer features with greater identification ability of the target category. Moreover, the universality of the proposed algorithm proves to be available with the experiment that every category is set to be the target one.