Dan Zhang¹, Yu Zhang², Yiwen Liang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2317-2336, 2023, DOI:10.32604/cmc.2023.038026

Abstract The dendritic cell algorithm (DCA) is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system. Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA. The loss function of DCA is ambiguous due to its complexity. To reduce the uncertainty, several researchers simplified the algorithm program; some introduced gradient descent to optimize parameters; some utilized searching methods to find the optimal parameter combination. However, these studies are either time-consuming or need to be revised in the case of non-convex functions. To overcome the problems,… More >

Dan Zhang¹, Yiwen Liang^1,*, Hongbin Dong²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2025-2045, 2023, DOI:10.32604/cmes.2023.022864

Abstract The artificial immune system, an excellent prototype for developing Machine Learning, is inspired by the function of the powerful natural immune system. As one of the prevalent classifiers, the Dendritic Cell Algorithm (DCA) has been widely used to solve binary problems in the real world. The classification of DCA depends on a data pre-processing procedure to generate input signals, where feature selection and signal categorization are the main work. However, the results of these studies also show that the signal generation of DCA is relatively weak, and all of them utilized a filter strategy to remove unimportant attributes. Ignoring filtered… More > Graphic Abstract