TY - GEN
T1 - Dendritic cell algorithm with fuzzy inference system for input signal generation
AU - Elisa, Noe
AU - Li, Jie
AU - Zuo, Zheming
AU - Yang, Longzhi
PY - 2018/8/11
Y1 - 2018/8/11
N2 - Dendritic cell algorithm (DCA) is a binary classification system developed by abstracting the biological danger theory and the functioning of human dendritic cells. The DCA takes three signals as inputs, including danger, safe and pathogenic associated molecular pattern (PAMP), which are generated in its pre-processing and initialization phase. In particular, after a feature selection process for a given training data set, each selected attribute is assigned to one of the three input signals. Then, these input signals are calculated as the aggregation of their associated features, usually implemented by a simple average function followed by a normalisation process. If a nonlinear relationship exists between a signal and its corresponding selected attributes, the resulting signal using the average function may negatively affect the classification results of the DCA. This work proposes an approach named TSK-DCA to address such limitation by aggregating the assigned features of a signal linearly or non-linearly depending on their inherit relationship using the TSK+ fuzzy inference system. The proposed approach was evaluated and validated using the popular KDD99 data set, and the experimental results indicate the superiority of the proposed approach compared to its conventional counterpart.
AB - Dendritic cell algorithm (DCA) is a binary classification system developed by abstracting the biological danger theory and the functioning of human dendritic cells. The DCA takes three signals as inputs, including danger, safe and pathogenic associated molecular pattern (PAMP), which are generated in its pre-processing and initialization phase. In particular, after a feature selection process for a given training data set, each selected attribute is assigned to one of the three input signals. Then, these input signals are calculated as the aggregation of their associated features, usually implemented by a simple average function followed by a normalisation process. If a nonlinear relationship exists between a signal and its corresponding selected attributes, the resulting signal using the average function may negatively affect the classification results of the DCA. This work proposes an approach named TSK-DCA to address such limitation by aggregating the assigned features of a signal linearly or non-linearly depending on their inherit relationship using the TSK+ fuzzy inference system. The proposed approach was evaluated and validated using the popular KDD99 data set, and the experimental results indicate the superiority of the proposed approach compared to its conventional counterpart.
KW - Danger theory
KW - Dendritic cell algorithm
KW - Information aggregation
KW - TSK+ fuzzy inference system
U2 - 10.1007/978-3-319-97982-3_17
DO - 10.1007/978-3-319-97982-3_17
M3 - Conference contribution
AN - SCOPUS:85052242077
SN - 9783319979816
T3 - Advances in Intelligent Systems and Computing
SP - 203
EP - 214
BT - Advances in Computational Intelligence Systems
A2 - Lotfi, Ahmad
A2 - Bouchachia, Hamid
A2 - Gegov, Alexander
A2 - Langensiepen, Caroline
A2 - McGinnity, Martin
PB - Springer
T2 - 18th UK Workshop on Computational Intelligence, UKCI 2018
Y2 - 5 September 2018 through 7 September 2018
ER -