不同机器学习算法在信用卡欺诈检测中的性能分析

Amanpreet Kaur, Vansh Sachdeva, Abhijot Singh, Ayush Jaiswal, Niyati Aggrawal, Archana Purwar
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引用次数: 0

摘要

机器学习(ML)是对各种算法和事实模型的逻辑研究,pc利用这些算法和模型来执行未明确编程的特定操作。本文旨在统计分析不同的机器学习算法,并比较和对比它们在信用卡欺诈检测中的性能。使用的算法有人工神经网络(ANN)、样本向量机(SVM)、第k近邻(KNN)、决策树、逻辑回归和随机森林。在性能指标的基础上对上述算法进行了比较。推导出随机森林算法是最佳算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Analysis of Different Machine Learning Algorithms on Credit Card Fraud Detection
Machine learning (ML) is a logical investigation of various algorithms and factual models that PCs utilize to carry out particular operations that are not clearly programmed. This paper aims to statistically analyze different machine learning algorithms, and compare and contrast their performance for credit card fraud detection. Algorithms used are Artificial Neural Networks(ANN), Sample Vector Machine (SVM), and Kth Nearest Neighbour (KNN), Decision Tree, Logistic Regression and Random Forest. All these above mentioned algorithms are compared on basis of performance measures. It is deduced that the random forest algorithm is the best algorithm.
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