Identification and Analysis of Email Spam using Filtering Techniques
Ananta Charan Ojha, Ajay Chakravarty · 2023
The majority of transactions and commerce in this e-world use emails. Due to the time and money savings, email has developed into a strong tool for communication today. However, the majority of emails now include spam, which is undesired information, thanks to social networks and advertisements. Although several algorithms have been created for the classification of email spam, none of them can categorise spam emails with 100% accuracy. In order to investigate the most effective classifier for email spam classification, the spam dataset is examined in this research utilising the TANAGRA data mining tool. Fisher filtering, FCBF, MOD tree, and MIFS are some of the classifiers used for identification and analysis. Finally, based on calculation time in seconds, the top classifier for email spam is determined.