%0 Journal Article %A YANG Xinmin %A DONG Hongbin %A TAN Chengyu %A ZHOU Wen %T Dendritic Cell Model Using Singular Value Decomposition and Information Gain %D 2021 %R 10.3778/j.issn.1002-8331.2004-0251 %J Computer Engineering and Applications %P 156-162 %V 57 %N 15 %X

Aiming at the problem that the signal extraction process of the Dendritic Cell Algorithm(DCA) is affected by artificial experience and its ability to detect anomalies in disordered data is not strong, SIDCA, a Singular Value Decomposition(SVD) and Information Gain(IG) dendritic cell model is proposed. SIDCA uses the SVD method to obtain the most relevant feature subset, and then uses the information gain to extract the most relevant feature in the most relevant feature subset to realize adaptive signal extraction and reduce the disorder of data to the algorithm confused. Experimental comparisons with classic DCA and deterministic DCA(dDCA)show that SIDCA has higher accuracy and lower false alarm rate on ordered and unordered data sets.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2004-0251