An upper and lower CUSUM for signal normalization in the dendritic cell algorithm

Mohamad Farhan Mohamad Mohsin, Abdul Razak Hamdan, Azuraliza Abu Bakar

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Signal normalization is a part of signal formalization which is a vital data pre-processing constraint required for the functioning of the dendritic cell algorithm. In existing applications, most normalization algorithms are developed purposely for a specific application with drawing on human domain expertise and very few algorithms are designed for general problems. This makes it difficult for the inexperienced user to exploit existing approaches to another problem, particularly when the initial information about the problem is limited. Therefore, this study proposes a new signal normalization method for the dendritic cell algorithm that uses the statistical upper and lower cumulative sum so that the algorithm can be applied to general classification problems. In addition, a new method to calculate the anomaly threshold based on the average mature-contact antigen value is presented to suit the proposed algorithm. The proposed model is evaluated by applying it to eight universal classification datasets and assessing its performance according to four measurement metrics: detection rate, specificity, false alarm rate, and accuracy. Its performance is compared with that of the existing dendritic cell algorithm and four non-bio-inspired classifiers, namely, rough set, decision tree, naïve Bayes, and multilayer perceptron. The results show that the proposed model outperforms the existing model and the other classifiers as well as demonstrates a significant improvement in terms of specificity, false alarm rate, and accuracy for all datasets. This indicates that the proposed normalization approach can be applied to general classification problems and can improve detection performance.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalEvolutionary Intelligence
DOIs
Publication statusAccepted/In press - 20 Apr 2016

Fingerprint

Dendritic Cells
Cumulative Sum
Normalization
False Alarm Rate
Classification Problems
Specificity
Classifiers
Classifier
Decision Trees
Data Preprocessing
Neural Networks (Computer)
Multilayer neural networks
Bayes
Antigens
Decision trees
Expertise
Perceptron
Rough Set
Formalization
Decision tree

Keywords

  • Artificial immune system
  • Danger theory
  • Dendritic cell algorithm
  • Signal normalization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Computer Vision and Pattern Recognition
  • Mathematics (miscellaneous)

Cite this

An upper and lower CUSUM for signal normalization in the dendritic cell algorithm. / Mohsin, Mohamad Farhan Mohamad; Hamdan, Abdul Razak; Abu Bakar, Azuraliza.

In: Evolutionary Intelligence, 20.04.2016, p. 1-15.

Research output: Contribution to journalArticle

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