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An improved artificial dendrite cell algorithm for abnormal signal detection

Mohamad Mohsin, Mohamad Farhan and Abu Bakar, Azuraliza and Hamdan, Abdul Razak and Abdul Wahab, Mohd Helmy (2018) An improved artificial dendrite cell algorithm for abnormal signal detection. Journal of Information and Communication Technology (JICT), 17 (1). pp. 33-54. ISSN 21803862

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Abstract

point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.

Item Type: Article
Uncontrolled Keywords: Anomaly threshold; dendrite cell algorithm; multi-context antigen value
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 23 Jun 2019 06:53
Last Modified: 23 Jun 2019 06:53
URI: http://eprints.uthm.edu.my/id/eprint/11570
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