Effect of Feature Ranking on the Detection of Credit Card Fraud: Comparative Evaluation of Four Techniques

John, Oloruntoba Awoyemi; Adebayo, Adetunmbi; Samuel, Oluwadare

doi:10.26634/jpr.5.3.15676

Cited by 4 publications

(1 citation statement)

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“…The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets. real-world situation faced because most of the time even for an important dataset such as cancer detection [14] and bank fraud [15], the data instances are scarce. It can be expensive if the new data needs labeling [16].…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Amri

Ismail

Mohammad

2019

Int. J. Adv. Intell. Informatics

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Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.

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Section: A R T I C L E I N F Omentioning

confidence: 99%