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About Electricity Fraud Detection Using Committee Semi-Supervised Learning
This document describes research on using a semi-supervised learning framework called co-training by committee (CoBC) for electricity fraud detection. CoBC starts by training a random forest classifier on labeled data, then iteratively adds the most confidently classified unlabeled data to the training set. When evaluated on a smart meter dataset with 5% positive class balance and 90% unlabeled data, the CoBC model achieved 84% true positive rate, 11% false positive rate, and 0.89 AUC. The goal is to improve fraud detection performance by leveraging both labeled and unlabeled data when only a small portion of the data is labeled due to the limited number of utility inspections.
Detailed Information
| Author: | ['naitsircdivad'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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