The Application of Online Classifier Ensembles in E-mail Spam Detection

Alberto Verdecia-Cabrera (Speaker), Isvani Frías-Blanco (Speaker), Arco Garcia, L. (Speaker), Yanet Rodríguez-Sarabia (Speaker), Asiel Díaz-Benítez (Speaker), Agustín Ortiz-Díaz (Speaker)

Activity: Talk or presentationTalk or presentation at a conference


Internet of Things, the connection of objects such as computing machines, embedded devices, equipment, appliances, and sensors to the Internet, are generating a huge quantity of data in real time. Many of these devices connected to the internet can be used for the generation of spam emails, which can affect both companies and individual users. Because of that, e-mail servers need to be updated to detect in an efficient way these messages. To analyze these emails, there have been proposed various approaches using traditional data mining learning algorithms. But these algorithms require having the data previously stored in order to classify them. Due to the temporal dimension of the data and the dynamism of these spam emails, the target function to be learned can change over time, a problem commonly known as concept drift. In this paper we propose an approach to filter spam mails based on online ensemble classifiers. The predictive performance of these algorithms is evaluated on the benchmark corpus constructed in this work. The experimental results show that that online ensemble algorithms.
Period28 Jun 201930 Jun 2019
Event title2nd International Scientific Convention: Workshop 2019 on Internet of Things and Artificial Intelligence
Event typeConference
Conference number1
LocationVilla Clara, Cuba
Degree of RecognitionInternational