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Fraud Detection Techniques Comparison

Credit card Fraud DBSCAN Box plot Z-score
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This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the techniques are evaluated on the same test set, and reported in terms of Recall and Precision and execution time.

External resources

  • Four Techniques for Outlier Detection
  • Fraud Detection using Random Forest, Neural Autoencoder, and Isolation Forest Techniques
  • Credit Card Fraud Detection on Kaggle
  • Overview of Credit Card Fraud Detection Techniques

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.1 Note: Not all extensions may be displayed.
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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME H2O Machine Learning Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.1

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    KNIME JavaScript Views (Labs) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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    KNIME Optimization extension Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

    knime
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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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