Volume 11 - Volume 11
Comparative Analysis of Random Forest Classification Over SVM Classifier to Detect Cyber Thefts in Credit Card to Reduce False Rate
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.
Paper Details
PaperID: 1761
Author's Name: K.R. Ruthvik and Dr.G. Charlyn
Volume: Volume 11
Issues: Volume 11
Keywords: False Rate Reduction, Efficient Approach, Machine Learning, Novel Detection for Cyber Threats, Unique Approach for False Rate Reduction.
Year: 2021
Month: April
Pages: 1339-1348