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Fraud_​credit_​card

Keras Autoencoder Architecture
Data Preprocessing
Training the Autoencoder
Optimizing threshold K
Final Performance
70% of negativesfor training
Table Partitioner
1/3 of negatives and all positives for validation
Concatenate
Min-max normalization
Normalizer
Variable to Table Row
Table Writer
Normalizer (Apply)
Shape: 7
Keras Input Layer
Units: 5Activation:ReLu
Keras Dense Layer
Units:15Activation: ReLu
Keras Dense Layer
Units: 7Activation: Sigmoid
Keras Dense Layer
Units: 8Activation:ReLu
Keras Dense Layer
10 % for validation
Table Partitioner
Units: 15Activation:ReLu
Keras Dense Layer
Scorer
Statistics
Train with Loss function=MSE Optimizer=Adam
Keras Network Learner
Rule Engine
Units: 8Activation: ReLU
Keras Dense Layer
Row Sampler
Normalizer
Numeric Distances
DBSCAN
Apply network
Keras Network Executor
Read credit card data
CSV Reader
Classifytransactions based onthreshold
Rule Engine
Class
Number to String
ROC Curve
Rule Engine
Top:Class = 0
Row Splitter
Threshold Optimization
Scorer
Normalizer (Apply)
Number to String
Statistics
Statistics
Math Formula

Nodes

Extensions

Links

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: elodi
Created at: 2026-05-15
On NodePit since: 2026-05-16
Last update: 2026-05-16
Created with KNIME version: v5.11.0

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