FdDCA

A Novel Fuzzy Deterministic Dendritic Cell Algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Downloads (Pure)

Abstract

The Dendritic Cell Algorithm (DCA) and its improved version: Deterministic Dendritic Cell Algorithm (dDCA) are essentially binary classification algorithms based on the behavior of Dendritic Cells (DCs) in the immune system. Both DCA and dDCA collect and process the data in form of signals, and produce output signal. The signals are divided in two types: danger and safe signals, and the output signal is determined by the values of the danger and safe signals. However, both DCA and dDCA suffer from data misclassification due to their sensitivity to data order. In this research we proposed a Fuzzy Deterministic Dendritic Cell Algorithm (FdDCA), which combines dDCA, fuzzy sets, and K-means clustering. The main objective of this research is to smooth the sharp boundaries between signals since we cannot always identify a clear boundary between the values of the signals. Our approach fuzzifies the signal values using linguistic variables, and a rule base is built to support fuzzy inference. The experimental results based on real data sets show that our approach shows a promising results compared to DCA and dDCA.
Original languageEnglish
Title of host publicationGECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
EditorsTobias Friedrich, Frank Neumann, Andrew M. Sutton
PublisherACM
Pages1007-1010
Number of pages4
ISBN (Print)978-1-4503-4323-7
DOIs
Publication statusPublished - 20 Jul 2016

Fingerprint

Dendritic Cells
Immune system
Fuzzy inference
Fuzzy sets
Linguistics

Keywords

  • artificial immune systems
  • deterministic Dendritic Cell algorithm
  • fuzzy logic

Cite this

Mukhtar, N., Coghill, G. M., & Pang, W. (2016). FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. In T. Friedrich, F. Neumann, & A. M. Sutton (Eds.), GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion (pp. 1007-1010). ACM. https://doi.org/10.1145/2908961.2931662

FdDCA : A Novel Fuzzy Deterministic Dendritic Cell Algorithm. / Mukhtar, Nura; Coghill, George M.; Pang, Wei.

GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ed. / Tobias Friedrich; Frank Neumann; Andrew M. Sutton. ACM, 2016. p. 1007-1010.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mukhtar, N, Coghill, GM & Pang, W 2016, FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. in T Friedrich, F Neumann & AM Sutton (eds), GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM, pp. 1007-1010. https://doi.org/10.1145/2908961.2931662
Mukhtar N, Coghill GM, Pang W. FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. In Friedrich T, Neumann F, Sutton AM, editors, GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM. 2016. p. 1007-1010 https://doi.org/10.1145/2908961.2931662
Mukhtar, Nura ; Coghill, George M. ; Pang, Wei. / FdDCA : A Novel Fuzzy Deterministic Dendritic Cell Algorithm. GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. editor / Tobias Friedrich ; Frank Neumann ; Andrew M. Sutton. ACM, 2016. pp. 1007-1010
@inproceedings{c5c3476c48d94ea0bd30353b3138238b,
title = "FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm",
abstract = "The Dendritic Cell Algorithm (DCA) and its improved version: Deterministic Dendritic Cell Algorithm (dDCA) are essentially binary classification algorithms based on the behavior of Dendritic Cells (DCs) in the immune system. Both DCA and dDCA collect and process the data in form of signals, and produce output signal. The signals are divided in two types: danger and safe signals, and the output signal is determined by the values of the danger and safe signals. However, both DCA and dDCA suffer from data misclassification due to their sensitivity to data order. In this research we proposed a Fuzzy Deterministic Dendritic Cell Algorithm (FdDCA), which combines dDCA, fuzzy sets, and K-means clustering. The main objective of this research is to smooth the sharp boundaries between signals since we cannot always identify a clear boundary between the values of the signals. Our approach fuzzifies the signal values using linguistic variables, and a rule base is built to support fuzzy inference. The experimental results based on real data sets show that our approach shows a promising results compared to DCA and dDCA.",
keywords = "artificial immune systems, deterministic Dendritic Cell algorithm, fuzzy logic",
author = "Nura Mukhtar and Coghill, {George M.} and Wei Pang",
year = "2016",
month = "7",
day = "20",
doi = "10.1145/2908961.2931662",
language = "English",
isbn = "978-1-4503-4323-7",
pages = "1007--1010",
editor = "Friedrich, {Tobias } and Neumann, {Frank } and Sutton, {Andrew M. }",
booktitle = "GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion",
publisher = "ACM",

}

TY - GEN

T1 - FdDCA

T2 - A Novel Fuzzy Deterministic Dendritic Cell Algorithm

AU - Mukhtar, Nura

AU - Coghill, George M.

AU - Pang, Wei

PY - 2016/7/20

Y1 - 2016/7/20

N2 - The Dendritic Cell Algorithm (DCA) and its improved version: Deterministic Dendritic Cell Algorithm (dDCA) are essentially binary classification algorithms based on the behavior of Dendritic Cells (DCs) in the immune system. Both DCA and dDCA collect and process the data in form of signals, and produce output signal. The signals are divided in two types: danger and safe signals, and the output signal is determined by the values of the danger and safe signals. However, both DCA and dDCA suffer from data misclassification due to their sensitivity to data order. In this research we proposed a Fuzzy Deterministic Dendritic Cell Algorithm (FdDCA), which combines dDCA, fuzzy sets, and K-means clustering. The main objective of this research is to smooth the sharp boundaries between signals since we cannot always identify a clear boundary between the values of the signals. Our approach fuzzifies the signal values using linguistic variables, and a rule base is built to support fuzzy inference. The experimental results based on real data sets show that our approach shows a promising results compared to DCA and dDCA.

AB - The Dendritic Cell Algorithm (DCA) and its improved version: Deterministic Dendritic Cell Algorithm (dDCA) are essentially binary classification algorithms based on the behavior of Dendritic Cells (DCs) in the immune system. Both DCA and dDCA collect and process the data in form of signals, and produce output signal. The signals are divided in two types: danger and safe signals, and the output signal is determined by the values of the danger and safe signals. However, both DCA and dDCA suffer from data misclassification due to their sensitivity to data order. In this research we proposed a Fuzzy Deterministic Dendritic Cell Algorithm (FdDCA), which combines dDCA, fuzzy sets, and K-means clustering. The main objective of this research is to smooth the sharp boundaries between signals since we cannot always identify a clear boundary between the values of the signals. Our approach fuzzifies the signal values using linguistic variables, and a rule base is built to support fuzzy inference. The experimental results based on real data sets show that our approach shows a promising results compared to DCA and dDCA.

KW - artificial immune systems

KW - deterministic Dendritic Cell algorithm

KW - fuzzy logic

U2 - 10.1145/2908961.2931662

DO - 10.1145/2908961.2931662

M3 - Conference contribution

SN - 978-1-4503-4323-7

SP - 1007

EP - 1010

BT - GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

A2 - Friedrich, Tobias

A2 - Neumann, Frank

A2 - Sutton, Andrew M.

PB - ACM

ER -