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Fraud_​credit_​card_​final

Keras Autoencoder Architecture
Data Preprocessing
Training the Autoencoder
Optimizing threshold K
Final Performance

DBSCAN Clustering

Exploration Data

Random Forest

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
Table Partitioner
Units: 8Activation:ReLu
Keras Dense Layer
10 % for validation
Table Partitioner
Units: 15Activation:ReLu
Keras Dense Layer
Scorer
Statistics
Normalizer
Train with Loss function=MSE Optimizer=Adam
Keras Network Learner
Random Forest Learner
Rule Engine
Random Forest Predictor
Units: 8Activation: ReLU
Keras Dense Layer
Row Sampler
Normalizer
Numeric Distances
DBSCAN
Apply network
Keras Network Executor
Number to String
Read credit card data
CSV Reader
Normalizer (Apply)
Classifytransactions based onthreshold
Rule Engine
Class
Number to String
ROC Curve
Rule Engine
Top:Class = 0
Row Splitter
Scorer
Threshold Optimization
Scorer
Number to String
Normalizer (Apply)
Number to String
Bar Chart
Box Plot
Statistics
Box Plot
Math Formula
Normalizer
Statistics

Nodes

Extensions

Links

Download

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: 2026-05-17
Last update: 2026-05-20
Created with KNIME version: v5.11.0

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