Comparative Analysis of Random Forest Classification Over SVM Classifier to Detect Cyber Thefts in Credit Card to Reduce False Rate

Authors

  • K.R. Ruthvik
  • Dr.G. Charlyn

DOI:

https://doi.org/10.47059/revistageintec.v11i2.1761

Abstract

Aim: To reduce the false rate of cyber thefts in credit card attacks based on binary selection Random Forest classifier and SVM classifier. Materials and Methods: Classification is performed by Random forest classifier (N=28) over SVM classifier (N=28) is for false rate detection. Results and Discussion: The values obtained in terms of accuracy is identified by random state in Random forest (94.4%) over SVM (91.4%) Conclusion: The reduction of false rate with sigma value 0.126 appears to be better in Random Forest classifier than SVM classifier.

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Published

2021-06-03

Issue

Section

Articles