An adaptive anomaly threshold in artificial dendrite cell algorithm
The dendrite cell algorithm (DCA) relies on the multi-context antigen value (MCAV) to determine the abnormality of a record by comparing it with anomaly threshold.In practice, the threshold is pre-determined before mining based on previous information and the existing MCAV is inefficient when expos...
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my.uum.repo.228362017-07-27T01:09:42Z http://repo.uum.edu.my/22836/ An adaptive anomaly threshold in artificial dendrite cell algorithm Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak QA75 Electronic computers. Computer science The dendrite cell algorithm (DCA) relies on the multi-context antigen value (MCAV) to determine the abnormality of a record by comparing it with anomaly threshold.In practice, the threshold is pre-determined before mining based on previous information and the existing MCAV is inefficient when expose to extreme values.This causes the DCA fails to detect unlabeled data if the new pattern distinct from previous information and reduces the detection accuracy.This paper proposed an adaptive anomaly threshold 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 datasets, the new version of DCA generated a better detection result than its previous version in term of sensitivity, specificity, false detection rate, and accuracy. 2017-04-25 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/22836/1/ICOCI%202017%20250-255.pdf Mohamad Mohsin, Mohamad Farhan and Abu Bakar, Azuraliza and Hamdan, Abdul Razak (2017) An adaptive anomaly threshold in artificial dendrite cell algorithm. In: 6th International Conference on Computing & Informatics (ICOCI2017), 25 - 27 April 2017, Kuala Lumpur. http://icoci.cms.net.my/PROCEEDINGS/2017/Pdf_Version_Chap04e/PID124-250-255e.pdf |
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QA75 Electronic computers. Computer science Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak An adaptive anomaly threshold in artificial dendrite cell algorithm |
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The dendrite cell algorithm (DCA) relies on the multi-context antigen value (MCAV) to determine the abnormality of a record by comparing it with anomaly threshold.In practice, the threshold is pre-determined before mining based on previous information and the existing MCAV is inefficient
when expose to extreme values.This causes the DCA fails to detect unlabeled data if the new pattern distinct from previous information and reduces
the detection accuracy.This paper proposed an adaptive anomaly threshold 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 datasets, the new version of DCA generated a better detection result than its previous version in term of sensitivity, specificity, false detection rate, and accuracy. |
format |
Conference or Workshop Item |
author |
Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak |
author_facet |
Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak |
author_sort |
Mohamad Mohsin, Mohamad Farhan |
title |
An adaptive anomaly threshold in artificial dendrite cell algorithm |
title_short |
An adaptive anomaly threshold in artificial dendrite cell algorithm |
title_full |
An adaptive anomaly threshold in artificial dendrite cell algorithm |
title_fullStr |
An adaptive anomaly threshold in artificial dendrite cell algorithm |
title_full_unstemmed |
An adaptive anomaly threshold in artificial dendrite cell algorithm |
title_sort |
adaptive anomaly threshold in artificial dendrite cell algorithm |
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2017 |
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http://repo.uum.edu.my/22836/1/ICOCI%202017%20250-255.pdf http://repo.uum.edu.my/22836/ http://icoci.cms.net.my/PROCEEDINGS/2017/Pdf_Version_Chap04e/PID124-250-255e.pdf |
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