Icon

Projeto_​MABD11

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)
Missing Value
Date&Time Part Extractor
String to Date&Time
Rule Engine
Rule Engine
10 % for validation
Table Partitioner
Random Forest Predictor
One to Many
Random Forest Learner
Train with Loss function=MSE Optimizer=Adam
Keras Network Learner
Write model
Keras Network Writer
Column Filter
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

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!