Zainal K.Jali M.Z.2024-05-282024-05-282017978151000000010.1109/ICoICT.2017.80746882-s2.0-85037586736https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037586736&doi=10.1109%2fICoICT.2017.8074688&partnerID=40&md5=707f48ccf5f7ebd65e5dbff80a214070https://oarep.usim.edu.my/handle/123456789/8813The 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.en-USdendritic cell algorithmfeature selection methodsspam risk concentrationspam severity assessmentterm weighting schemesCellsFeature extractionDendritic cell algorithmsDendritic cell algorithms (DCA)Feature selection methodsInformation gain ratioOn-line documentationsRisk classificationspam severity assessmentTerm weighting schemeRisk assessmentConference Paper8074688