Synergistic Detection of SMS Spam: Harnessing the Power of Hybrid Voting Technique

Abstract

Due to the variety of spamming techniques used, detecting SMS spam is a difficult task. This research study suggests a novel approach to improving SMS spam detection accuracy by leveraging the power of hybrid voting techniques. This research aims to combine the outputs of various machine learning models. Experiment results on a publicly available dataset show that the proposed hybrid voting technique outperforms individual models, detecting SMS spam with a high accuracy of over 98%. This approach has a lot of potential for improving SMS spam detection and can be applied to other types of spam detection tasks in different domains.