• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Risk based bagged ensemble (RBE) for credit card fraud detection
【24h】

Risk based bagged ensemble (RBE) for credit card fraud detection

机译：基于风险的袋装集成（RBE）用于信用卡欺诈检测

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Credit card frauds costs consumers several billions of dollars annually. Even with several systems in place accurate fraud detections remains unsolved, due to several intrinsic issues contained in transaction data. This paper analyzes the intrinsic nature of data and proposes a risk based ensemble model RBE as a solution to handle the intrinsic issues contained in data and also to provide effective results. The conventional bagging model is extended and novel enhancements have been incorporated in terms of an effective base learner and a cost sensitive combiner. Bagging models are highly efficient in handling imbalanced data, while incorporation of risk based Naïve Bayes handles the implicit noise contained in transaction data. Cost sensitive combiner replaces the conventional voting combiner to produce results that exhibits high performances and low cost. Comparisons with state-of-the-art models indicates the high performance levels of the proposed RBE model.
机译：信用卡欺诈每年使消费者损失数十亿美元。即使存在多个系统，由于交易数据中包含多个固有问题，仍无法解决准确的欺诈检测。本文分析了数据的内在本质，并提出了一种基于风险的集成模型RBE，作为处理数据中所包含的内在问题并提供有效结果的解决方案。扩展了传统的套袋模型，并结合了有效的基础学习者和成本敏感的组合器方面的新颖增强功能。套袋模型在处理不平衡数据方面非常高效，而基于风险的朴素贝叶斯的合并处理了交易数据中包含的隐式噪声。成本敏感型合并器替代了传统的投票合并器，以产生具有高性能和低成本的结果。与最新模型的比较表明所提出的RBE模型具有较高的性能水平。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号