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Spammer detection using multi-classifier information fusion based on evidential reasoning rule.

Shuaitong Liu1, Xiaojun Li2, Changhua Hu1

  • 1High-Tech Institute of Xi'an, Xi'an, 710025, Shaanxi, China.

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This study introduces a new evidential reasoning (ER) model for improved spammer detection. It effectively fuses information from multiple classifiers by considering their reliability and importance, enhancing classification accuracy.

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Spammer detection is crucial for user authenticity verification and is treated as a classification problem.
  • Multi-classifier information fusion enhances spammer detection but often neglects classifier uncertainty, reliability, and importance.

Purpose of the Study:

  • To develop a novel multi-classifier information fusion model for effective spammer detection.
  • To address limitations in existing fusion strategies by incorporating uncertainty and classifier reliability.

Main Methods:

  • Constructed base classifiers using profile-based, content-based, and behavior-based user characterization.
  • Developed a fusion model integrating multi-classifier fusion with the evidential reasoning (ER) rule.
  • Aggregated base classifier results by considering their weights and reliabilities.

Main Results:

  • The proposed evidential reasoning (ER) based fusion model demonstrated effectiveness in spammer detection.
  • Experimental results on a real-world dataset validated the model's performance.
  • The model successfully accounts for uncertainty and distinguishes the importance and reliability of individual classifiers.

Conclusions:

  • The novel multi-classifier information fusion model based on evidential reasoning (ER) rule offers a significant improvement for spammer detection.
  • This approach provides a more robust method for integrating diverse classifier outputs by managing uncertainty and weighting classifier contributions.
  • The findings highlight the potential of ER in enhancing classification tasks where multiple, potentially uncertain, information sources are involved.