A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Authors

  • Swetha Chikkasabbenahalli Venkatesh School of Computational Sciences & IT, Garden City University, Bangalore, India
  • Sibi Shaji School of Computational Sciences & IT, Garden City University, Bangalore, India
  • Balasubramanian Meenakshi Sundaram Department of Computer Science & Engineering, New Horizon College of Engineering, Bangalore, India

DOI:

https://doi.org/10.46604/peti.2024.13200

Keywords:

fake profile, online social networks, stacked ensemble, imbalanced dataset, cost-sensitive learning

Abstract

Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.

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Published

2024-01-31

How to Cite

[1]
Swetha Chikkasabbenahalli Venkatesh, Sibi Shaji, and Balasubramanian Meenakshi Sundaram, “A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification”, Proc. eng. technol. innov., Jan. 2024.

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Articles