• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Arabian Journal for Science and Engineering >RGNBC: Rough Gaussian Naïve Bayes Classifier for Data Stream Classification with Recurring Concept Drift
【24h】

RGNBC: Rough Gaussian Naïve Bayes Classifier for Data Stream Classification with Recurring Concept Drift

机译：RGNBC：具有重复概念漂移的数据流分类的粗糙高斯朴素贝叶斯分类器

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

Due to the necessity of performing classification in streaming environments, researchers have developed various stream classification methods by handling concept drift. But, recurring concept drift is a challenging problem in data stream as the dimension of the data is not static over the period of time. By considering the recurring concept drift, this paper proposes a new classifier model, called Rough Gaussian Na < ve Bayes Classifier (RGNBC) for the data stream classification. Here, two new contributions are made to handle the challenges of recurring concept drift. The first contribution is to utilize the rough set theory for detecting the concept drift. Then, gaussian na < ve classifier is modified mathematically to handle the dynamic data without using the historic data. Also, the classification is performed using the posterior probability and the objective function which considers the multiple criteria. The proposed RGNBC model is experimented with two large datasets, and the results are validated against the existing MReC-DFS algorithm using sensitivity, specificity and accuracy. From the results, we proved that the proposed RGNBC model obtained the maximum accuracy of 74.5 % while compared with the existing algorithm.
机译：由于在流环境中执行分类的必要性，研究人员通过处理概念漂移开发了各种流分类方法。但是，由于数据的维度在一段时间内不是静态的，因此反复出现的概念漂移在数据流中是一个具有挑战性的问题。通过考虑递归概念的漂移，本文提出了一种新的分类器模型，称为数据流分类的粗糙高斯贝叶斯分类器（RGNBC）。在此，为应对反复出现的概念漂移带来了两个新的贡献。第一个贡献是利用粗糙集理论来检测概念漂移。然后，对高斯分类器进行数学修改以处理动态数据，而无需使用历史数据。同样，使用后验概率和考虑了多个标准的目标函数进行分类。对提出的RGNBC模型进行了两个大型数据集实验，并使用灵敏度，特异性和准确性针对现有MReC-DFS算法对结果进行了验证。从结果可以证明，与现有算法相比，所提出的RNGNB模型获得了74.5％的最大精度。

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