FRAUD DETECTION USING PARTICLE SWARM OPTIMIZATION-BASED SUPPORT VECTOR MACHINE
Fraud is a criminal deception committed by Bank or E-commerce user intended to result in personal gain. Fraud detection is one of an important thing that the Bank and E-commerce must be concerned about. Fraud must be able to be detected by Bank or E-commerce to minimize losses caused by this acti...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/38979 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Fraud is a criminal deception committed by Bank or E-commerce user intended to
result in personal gain. Fraud detection is one of an important thing that the Bank
and E-commerce must be concerned about. Fraud must be able to be detected by
Bank or E-commerce to minimize losses caused by this action.
Support Vector Machine or SVM is a classification method in machine learning
which can be used to classify a transaction as a fraud or not fraud. To use SVM, it
requires estimation of several hyperparameters values to get the best result. Particle
Swarm Optimization or PSO is a swarm-based optimization method which can be
used to search the best value for a specific objective function.
In this research, the author used PSO to find the best hyperparameter values of SVM
that can give the best model performance and able to classify the transaction data as
fraud or not. |
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