An Intelligent Ensemble Classification Method For Spam Diagnosis in Social Networks

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Ali Ahraminezhad 1 Musa Mojarad 2,* Hassan Arfaeinia 1

1. Department of Computer Engineering, Liyan Institute of Education, Bushehr, Iran

2. Department of Computer Engineering, Firoozabad Branch, Islamic Azad University, Firoozabad, Iran

* Corresponding author.


Received: 10 Oct. 2021 / Revised: 9 Nov. 2021 / Accepted: 2 Dec. 2021 / Published: 8 Feb. 2022

Index Terms

Spam detection, social networks, ensemble classification, intelligent technique


In recent years, the destructive behavior of social networks spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have extracted the behavioral characteristics of spam and obtained good results based on machine learning algorithms to identify them. However, most of these studies use a single classification technique that often works differently for different spam data. In this paper, an intelligent ensemble classification method for social networks spam detection is introduced. The proposed heterogeneous ensemble learning framework is based on stack generalization and uses an evolutionary algorithm to improve the modeling process and reduce complexity. In particular, particle swarm optimization has been used as an evolutionary algorithm to optimize model parameters to reduce model complexity. These parameters include a subset of effective features and a subset of the most appropriate single classification techniques. The SPAM E-mail dataset used in this article contains the correct and effective features in spam prediction. Experimental results show that the proposed algorithm effectively improves the detection rate of spam and performs better than the methods used.

Cite This Paper

Ali Ahraminezhad, Musa Mojarad, Hassan Arfaeinia, "An Intelligent Ensemble Classification Method For Spam Diagnosis in Social Networks", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.1, pp.24-31, 2022. DOI: 10.5815/ijisa.2022.01.02


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