SADEM: An Effective Supervised Anomaly Detection Ensemble Model for Alert Account Detection

Hui Kuo Yang*, Bing Li Su, Wen Chih Peng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Anomaly detection has been an important research topic for a long time and has been applied to many real-world applications. However, due to the high cost of manually getting the instance label, researchers mostly resort to unsupervised or semi-supervised learning approaches. The supervised learning method has rarely been used in anomaly detection tasks. In this paper, we proposed a supervised learning ensemble method to detect alert accounts among transaction data. We solve the problem of low-confident predictions when the anomalies reside within normal data points. The ensemble model comprises the LightGBM and Multi-layer Perceptron (MLP) to synergize machine learning and neural network models. The proposed model preserves the result of high-confident predictions and improves the performance of low-confident predictions with the new features generated from encoding the leaf node of GBDT (Gradient Boosting Decision Tree). Our experiments on a real-world dataset show the effectiveness of the model when compared with the state-of-the-art methods.

Original languageEnglish
Pages (from-to)1185-1207
Number of pages23
JournalJournal of Information Science and Engineering
Issue number5
StatePublished - Sep 2023


  • alert account detection
  • anomaly detection
  • imbalanced classification
  • low-confident predictions
  • supervised learning


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