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Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection
- Source :
- Neurocomputing. 407:50-62
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- The classification problems with imbalanced datasets widely exist in real word. An Extreme Learning Machine is found unsuitable for imbalanced classification problems. This work applies a Weighted Extreme Learning Machine (WELM) to handle them. Its two parameters are found to affect its performance greatly. The aim of this work is to apply various intelligent optimization methods to optimize a WELM and compare their performance in imbalanced classification. Experimental results show that WELM with a dandelion algorithm with probability-based mutation can perform better than WELM with improved particle swarm optimization, bat algorithm, genetic algorithm, dandelion algorithm and self-learning dandelion algorithm. In addition, the proposed algorithm is applied to credit card fraud detection. The results show that it can achieve high detection performance.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
Cognitive Neuroscience
Credit card fraud
Particle swarm optimization
02 engineering and technology
Machine learning
computer.software_genre
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
Genetic algorithm
Mutation (genetic algorithm)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Bat algorithm
Extreme learning machine
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 407
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........af4be0f3d828c92a4aef0655ef1b93d6