...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of multiple-valued logic and soft computing >A General Data Mining Methodology Based on a Weighted Hierarchical Adaptive Voting Ensemble (WHAVE) Machine Learning Method
【24h】

A General Data Mining Methodology Based on a Weighted Hierarchical Adaptive Voting Ensemble (WHAVE) Machine Learning Method

机译：基于加权层次自适应投票集合（WHAVE）机器学习方法的通用数据挖掘方法

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

This paper presents a general data mining methodology based on a novel Weighted Hierarchical Adaptive Voting Ensemble (WHAVE) machine learning (ML) method. It was constructed using three individual ML methods based on Multiple-Valued Logic: Disjunctive Normal Form (DNF) rule based method, Decision Trees, Nave Bayes, and one method based on continuous representation: Support Vector Machines (SVM). The WHAVE method was demonstrated in applications for breast cancer, heart disease detection and stock market prediction with accuracies of 99.8%, 96.7% and 95.2% respectively. Results were compared with other methods and show that the WHAVE method accuracy is noticeably higher than those of the individual ML methods tested. This paper demonstrates the advantage of this new machine learning methodology based on a hierarchical ensemble.
机译：本文提出了一种基于新颖的加权层次自适应投票组合（WHAVE）机器学习（ML）方法的通用数据挖掘方法。它是使用基于多值逻辑的三种独立的ML方法构造而成的：基于离散范式（DNF）规则的方法，决策树，Nave Bayes和基于连续表示的一种方法：支持向量机（SVM）。 WHAVE方法在乳腺癌，心脏病检测和股市预测中的应用得到了证明，其准确度分别为99.8％，96.7％和95.2％。将结果与其他方法进行比较，结果表明WHAVE方法的准确性明显高于测试的单个ML方法的准确性。本文展示了这种基于分层集成的新型机器学习方法的优势。

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