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Credit Card Fraud Detection Using Online Boosting
Abstract:
Nowadays, data stream mining is a very hot and high attention research field due to the real-time industrial applications from different sources are generating amount of data continuously as the streaming style. To process these growing and large data streams, data stream mining, classification algorithms have been proposed. These algorithms have to deal with high processing time and memory costs, class imbalance, overfitting and concept drift and so on. It is sure that ensembles of classifiers are being effectively used to make improvement in the accuracy of single classifiers in either data mining or data stream mining. Thus, to get higher performance in prediction with largely no increasing memory and time costs, this paper proposes an Online Boosting(OLBoost) Approach, which is firstly use the Extremely Fast Decision Tree (EFDT) as base (weak) learner, in order to ensemble them into a single online strong learner. The experiments of the proposed method were carried out for credit card fraud detection domain with the sample benchmark datasets.
Technology:
- Java
- JDK
- Data Science
- MySql
Including Packages
- Supporting Softwares
- Source Code
- Documentation
- Presentation Slides
- System architecture
- Data Flow Diagram
- Screenshots
- Execution Procedure
- Database File
Specialization
- Video On Demand *
- Remote Connectivity *
- Code Customization *
- Document Customization *
- Online Support *
- Voice Conference*
- Video Tutorials *
- Readme File
*Condition Apply
| Technology | Java |
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