...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge-Based Systems >Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions
【24h】

Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions

机译：通过序号的联合和分解功能进行分类，可解释可解释的AI和可解释机器学习解决方案

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 propose a novel classification according to aggregation functions of mixed behaviour by variability in ordinal sums of conjunctive and disjunctive functions. Consequently, domain experts are empowered to assign only the most important observations regarding the considered attributes. This has the advantage that the variability of the functions provides opportunities for machine learning to learn the best possible option from the data. Moreover, such a solution is comprehensible, reproducible and explainable-per-design to domain experts. In this paper, we discuss the proposed approach with examples and outline the research steps in interactive machine learning with a human-in-the-loop over aggregation functions. Although human experts are not always able to explain anything either, they are sometimes able to bring in experience, contextual understanding and implicit knowledge, which is desirable in certain machine learning tasks and can contribute to the robustness of algorithms. The obtained theoretical results in ordinal sums are discussed and illustrated on examples. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).commentSuperscript/Subscript Available/comment
机译：我们根据混合和分解功能的顺序变化的变异性，根据混合行为的聚集函数提出了一种新颖的分类。因此，域名专家被赋权仅为考虑的属性分配最重要的观察。这具有以下优点：函数的可变性为机器学习提供了从数据中学习最佳选择的机会。此外，对域专家来说，这种解决方案是可理解的，可重复的，可伸缩的，可伸缩的。在本文中，我们讨论了具有示例的提出的方法，并概述了交互式机器学习中的研究步骤，通过循环汇总函数。虽然人类专家并不总是能够解释任何事情，但它们有时能够带来经验，语境理解和隐含知识，这在某些机器学习任务中是可取的，并且可以促进算法的鲁棒性。在实施例上讨论和说明了所得序号的理论结果。 （c）2021提交人。由elsevier b.v发布。这是CC的开放式访问文章，由许可证（http://creativecommons.org/licenses/4.0/).&l ;comment＆ superscript/subscript可用＆ / compy

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