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Projeto_​MABD1

Keras Autoencoder for Fraud Detection - Training

This workflow trains an autoendcoder model to detect fraudulent transactions.

Keras Autoencoder Architecture
Data Preprocessing
Training the Autoencoder
Optimizing threshold K
Final Performance
2/3 of negatives for 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: 23
Keras Input Layer
Missing Value
Units: 20Activation: Sigmoid
Keras Dense Layer
Date&Time Part Extractor
Units: 25Activation: Sigmoid
Keras Dense Layer
String to Date&Time
Units: 15Activation: Sigmoid
Keras Dense Layer
Rule Engine
Units: 35Activation: Sigmoid
Keras Dense Layer
Rule Engine
10 % for validation
Table Partitioner
Units: 23Activation: Sigmoid
Keras Dense Layer
Units: 40Activation: Sigmoid
Keras Dense Layer
One to Many
Train with Loss function=MSE Optimizer=Adam
Keras Network Learner
Write model
Keras Network Writer
Scorer (JavaScript)
Counter Generation
Column Renamer
GroupBy
Column Filter
Apply network
Keras Network Executor
Rule Engine
Joiner
Read credit card data
CSV Reader
String Manipulation
Classifytransactions based onthreshold
Rule Engine
Math Formula
Class
Number to String
Rule Engine
Top:Class = 0
Row Splitter
String to Number
Threshold Optimization
Normalizer (Apply)
Normalizerfor deployment
Model Writer
Math Formula

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: Maarit Laukkanen
Created at: 2019-02-25
On NodePit since: 2026-05-11
Last update: 2026-05-11
Created with KNIME version: v5.11.0
Tags: autoencoderKerasneural networkdeep learningencoderdecoderreconstructioncredit card datafraud detectionanomaly detectionmodel evaluationbankingcredit cardfraudcybersecurity

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