当前位置: X-MOL 学术arXiv.cs.LO › 论文详情
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models
arXiv - CS - Logic in Computer Science Pub Date : 2020-10-15 , DOI: arxiv-2010.07650
Ioannis Mollas; Nick Bassiliades; Grigorios Tsoumakas

Interpretable machine learning is an emerging field providing solutions on acquiring insights into machine learning models' rationale. It has been put in the map of machine learning by suggesting ways to tackle key ethical and societal issues. However, existing techniques of interpretable machine learning are far from being comprehensible and explainable to the end user. Another key issue in this field is the lack of evaluation and selection criteria, making it difficult for the end user to choose the most appropriate interpretation technique for its use. In this study, we introduce a meta-explanation methodology that will provide truthful interpretations, in terms of feature importance, to the end user through argumentation. At the same time, this methodology can be used as an evaluation or selection tool for multiple interpretation techniques based on feature importance.
更新日期:2020-10-17

 

全部期刊列表>>
Springer 纳米技术权威期刊征稿
全球视野覆盖
施普林格·自然新
chemistry
3分钟学术视频演讲大赛
物理学研究前沿热点精选期刊推荐
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
ACS Publications填问卷
阿拉丁试剂right
麻省大学
西北大学
湖南大学
华东师范大学
王要兵
化学所
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
陆军军医大学
杨财广
廖矿标
试剂库存
down
wechat
bug