• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Information Technology & Decision Making >Feature Selection with Binary Symbiotic Organisms Search Algorithm for Email Spam Detection
【24h】

Feature Selection with Binary Symbiotic Organisms Search Algorithm for Email Spam Detection

机译：具有二元共生生物的特征选择，用于电子邮件垃圾邮件检测的搜索算法

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

One method to increase classifier accuracy is using Feature Selection (FS). The main idea in the FS is reducing complexity, eliminating irrelevant information, and deleting a subset of input features that either have little information or have no information for prediction. In this paper, three efficient binary methods based on the Symbiotic Organisms Search (SOS) algorithm were presented for solving the FS problem. In the first and second methods, several S_shaped and V_shaped transfer functions were used for the binarization of the SOS, respectively. These methods were called BSOSS and BSOSV. In the third method, two new operators called Binary Mutualism Phase (BMP) and Binary Commensalism Phase (BCP) were presented for binarization of the SOS, named Efficient Binary SOS (EBSOS). The proposed methods were run on 18 standard UCI datasets and compared to the base and important meta-heuristic algorithms. The test results showed that the EBSOS method has the best performance among the three proposed methods for the binarization of the SOS. Finally, the EBSOS method was compared to the Genetic Algorithm (GA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO) Algorithm, Binary Flower Pollination Algorithm (BFPA), Binary Grey Wolf Optimizer (BGWO) Algorithm, Binary Dragonfly Algorithm (BDA), and Binary Chaotic Crow Search Algorithm (BCCSA). In addition, the EBSOS method was executed on the spam email dataset with the KNN, NB, SVM, and MLP classifiers. The results showed that the EBSOS method has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, it was practically evaluated on spam email detection in particular.
机译：None

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