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  • 02_Fraud_Detection_by_Unsupervised_Learning
WorkflowWorkflow

Fraud Detection by Unsupervised Learning

Fraud Fraud detection Banking Credit card Cyber security
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This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transactions as outliers. The H2O Isolation Forest Predictor node produces two columns that can be used to identify outliers: outlier score and mean length. Here we identify outliers based on the mean length, which is the average number of random splits required to isolate a data point from the other data points. The threshold for the mean length is optimized using a parameter optimization loop.

External resources

  • Dataset on Kaggle
  • H2O Machine Learning Example Workflows
  • Four Techniques for Outlier Detection
  • Optimization Loop
  • Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.2
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

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    KNIME H2O Machine Learning Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Optimization extension Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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