Publication: The Significant Effect of Feature Selection Methods in Spam Risk Assessment Using Dendritic Cell Algorithm
| dc.Conferencecode | Multimedia Univ, Telkom Univ | |
| dc.Conferencedate | MAY 17-19, 2017 | |
| dc.Conferencelocation | MALAYSIA | |
| dc.Conferencename | 5th International Conference on Information and Communication Technology (ICoIC7) | |
| dc.contributor.author | Zainal, K | en_US |
| dc.contributor.author | Jali, MZ | en_US |
| dc.date.accessioned | 2024-05-29T02:50:11Z | |
| dc.date.available | 2024-05-29T02:50:11Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The 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. | |
| dc.identifier.scopus | WOS:000427146300049 | |
| dc.identifier.uri | https://oarep.usim.edu.my/handle/123456789/11012 | |
| dc.language | English | |
| dc.language.iso | en_US | |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2017 5th International Conference On Information And Communication Technology (Icoic7) | |
| dc.source | Web Of Science (ISI) | |
| dc.subject | term weighting schemes | en_US |
| dc.subject | feature selection methods | en_US |
| dc.subject | dendritic cell algorithm | en_US |
| dc.subject | spam severity assessment | en_US |
| dc.subject | spam risk concentration | en_US |
| dc.title | The Significant Effect of Feature Selection Methods in Spam Risk Assessment Using Dendritic Cell Algorithm | |
| dc.type | Proceedings Paper | en_US |
| dspace.entity.type | Publication |