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The significant effect of feature selection methods in spam risk assessment using dendritic cell algorithm

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cris.lastimport.scopus2024-12-06T16:44:32Z
dc.Conferencecode131305
dc.Conferencedate17 May 2017 through 19 May 2017
dc.Conferencename5th International Conference on Information and Communication Technology, ICoIC7 2017
dc.FundingDetailsMinistry of Higher Education, Malaysia,�MOHE: USIM/FRGS/FST/32/50315
dc.FundingDetailsThis research is fully funded by the Ministry of Higher Education of Malaysia with code USIM/FRGS/FST/32/50315.
dc.citedby3
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorZainal K.en_US
dc.contributor.authorJali M.Z.en_US
dc.date.accessioned2024-05-28T08:27:42Z
dc.date.available2024-05-28T08:27:42Z
dc.date.issued2017
dc.description.abstractThe vast amount of online documentation and the thriving of Internet especially mobile technology have caused a crucial demand to handle and organize unstructured data appropriately. An information retrieval or even knowledge discovery can be enhanced when a proper and structured data are available. This paper studies empirically the effect of pre-selected term weighting schemes, namely as Term Frequency (TF), Information Gain Ratio (IG Ratio) and Chi-Square (CHI2) in the assessment of a threat's impact loss. This feature selection method then further fed in conjunction with the Dendritic Cell Algorithm (DCA) as the classifier to measure the risk concentration of a spam message. The final outcome of this research is very much expected to be able in assisting people to make a decision once they knew the possible impact caused by a particular spam. The findings showed that TF is the best feature selection methods and well suited to be demonstrated together with the DCA, resulted with high accuracy risk classification rate. � 2017 IEEE.
dc.description.natureFinalen_US
dc.identifier.ArtNo8074688
dc.identifier.doi10.1109/ICoICT.2017.8074688
dc.identifier.isbn9781510000000
dc.identifier.scopus2-s2.0-85037586736
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85037586736&doi=10.1109%2fICoICT.2017.8074688&partnerID=40&md5=707f48ccf5f7ebd65e5dbff80a214070
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8813
dc.languageEnglish
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2017 5th International Conference on Information and Communication Technology, ICoIC7 2017
dc.sourceScopus
dc.subjectdendritic cell algorithmen_US
dc.subjectfeature selection methodsen_US
dc.subjectspam risk concentrationen_US
dc.subjectspam severity assessmenten_US
dc.subjectterm weighting schemesen_US
dc.subjectCellsen_US
dc.subjectFeature extractionen_US
dc.subjectDendritic cell algorithmsen_US
dc.subjectDendritic cell algorithms (DCA)en_US
dc.subjectFeature selection methodsen_US
dc.subjectInformation gain ratioen_US
dc.subjectOn-line documentationsen_US
dc.subjectRisk classificationen_US
dc.subjectspam severity assessmenten_US
dc.subjectTerm weighting schemeen_US
dc.subjectRisk assessmenten_US
dc.titleThe significant effect of feature selection methods in spam risk assessment using dendritic cell algorithm
dc.typeConference Paperen_US
dspace.entity.typePublication

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