An Improved Artificial Dendrite Cell Algorithm for Abnormal Signal Detection
In dendrite cell algorithm (DCA), the abnormality of a data 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 M...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia Press
2018
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/29124/1/JICT%2017%2001%202018%2033-54.pdf https://repo.uum.edu.my/id/eprint/29124/ |
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| Summary: | In dendrite cell algorithm (DCA), the abnormality of a data 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. |
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