PlumX Metrics
Embed PlumX Metrics

A heterogeneous ensemble learning framework for spam detection in social networks with imbalanced data

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 10, Issue: 3
2020
  • 60
    Citations
  • 0
    Usage
  • 62
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    60
    • Citation Indexes
      60
  • Captures
    62

Article Description

The popularity of social networks provides people with many conveniences, but their rapid growth has also attracted many attackers. In recent years, the malicious behavior of social network spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have mined the behavior characteristics of spammers and have obtained good results by applying machine learning algorithms to identify spammers in social networks. However, most of these studies overlook class imbalance situations that exist in real world data. In this paper, we propose a heterogeneous stacking-based ensemble learning framework to ameliorate the impact of class imbalance on spam detection in social networks. The proposed framework consists of two main components, a base module and a combining module. In the base module, we adopt six different base classifiers and utilize this classifier diversity to construct new ensemble input members. In the combination module, we introduce cost sensitive learning into deep neural network training. By setting different costs for misclassification and dynamically adjusting the weights of the prediction results of the base classifiers, we can integrate the input members and aggregate the classification results. The experimental results show that our framework effectively improves the spam detection rate on imbalanced datasets.

Bibliographic Details

Chensu Zhao; Yang Xin; Xuefeng Li; Yixian Yang; Yuling Chen

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know