Communications of the ACM
Statistical inference
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
The Deterministic Dendritic Cell Algorithm
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Integrating real-time analysis with the dendritic cell algorithm through segmentation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Further exploration of the fuzzy dendritic cell method
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
COID-FDCM: the fuzzy maintained dendritic cell classification method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A fuzzy-rough data pre-processing approach for the dendritic cell classifier
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
| Hi-index | 0.00 |
The Dendritic Cell Algorithm (DCA) is an immune inspired algorithm based on the behavior of dendritic cells. The performance of DCA depends on the selected features and their categorization to their specific signal types, during pre-processing. For feature selection, DCA applies the Principal Component Analysis (PCA). Nevertheless, PCA does not guarantee that the selected first principal components will be the most adequate for classification. Furthermore, the DCA categorization process is based on the PCA attributes' ranking in terms on variability. However, this categorization process could not be considered as a coherent assignment procedure. Thus, the aim of this paper is to develop a new DCA feature selection and categorization method based on Rough Set Theory (RST). In this model, the selection and the categorization processes are based on the RST CORE and REDUCT concepts. Results show that applying RST, instead of PCA, to DCA is more convenient for data pre-processing yielding much better performance in terms of accuracy.