• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Machine learning method for knowledge discovery experimented with otoneurological data.
【24h】

Machine learning method for knowledge discovery experimented with otoneurological data.

机译：机器学习方法，用于通过眼科学数据进行知识发现。

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

We have been interested in developing an otoneurological decision support system that supports diagnostics of vertigo diseases. In this study, we concentrate on testing its inference mechanism and knowledge discovery method. Knowledge is presented as patterns of classes. Each pattern includes attributes with weight and fitness values concerning the class. With the knowledge discovery method it is possible to form fitness values from data. Knowledge formation is based on frequency distributions of attributes. Knowledge formed by the knowledge discovery method is tested with two vertigo data sets and compared to experts' knowledge. The experts' and machine learnt knowledge are also combined in various ways in order to examine effects of weights on classification accuracy. The classification accuracy of knowledge discovery method is compared to 1- and 5-nearest neighbour method and Naive-Bayes classifier. The results showed that knowledge bases combining machine learnt knowledge with the experts' knowledge yielded the best classification accuracies. Further, attribute weighting had an important effect on the classification capability of the system. When considering different diseases in the used data sets, the performance of the knowledge discovery method and the inference method is comparable to other methods employed in this study.
机译：我们对开发支持眩晕疾病诊断的耳科决策支持系统感兴趣。在这项研究中，我们集中于测试其推理机制和知识发现方法。知识以班级形式呈现。每个模式都包含具有有关类别的权重和适合度值的属性。利用知识发现方法，可以从数据中形成适合度值。知识形成基于属性的频率分布。由知识发现方法形成的知识用两个眩晕数据集进行测试，并与专家的知识进行比较。专家和机器学习的知识也以各种方式组合在一起，以检查权重对分类准确性的影响。将知识发现方法的分类精度与1和5最近邻方法和朴素贝叶斯分类器进行了比较。结果表明，将机器学习的知识与专家的知识相结合的知识库产生了最佳的分类精度。此外，属性权重对系统的分类能力有重要影响。当考虑使用的数据集中的不同疾病时，知识发现方法和推理方法的性能可与本研究中使用的其他方法相媲美。

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