Steps towards causal Formal Concept Analysis

被引：3

作者：

Bazin, Alexandre ^{[1
]}

Couceiro, Miguel ^{[1
]}

Devignes, Marie-Dominique ^{[1
]}

Napoli, Amedeo ^{[1
]}

机构：

[1] Univ Lorraine, CNRS, INRIA, LORIA, Nancy, France

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2022年 / 142卷

基金：

欧盟地平线“2020”;

关键词：

Formal Concept Analysis; Causal inference; DUQUENNE-GUIGUES BASIS; CONCEPT LATTICES; DISCOVERY; INFERENCE; SETS;

D O I：

10.1016/j.ijar.2021.12.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A B S T R A C T Efficiently discovering causal relations from data and representing them in a way that facilitates their use is an important problem in science that has received much attention. In this paper, we propose an adaptation of the Formal Concept Analysis formalism to the problem of discovering and representing causal relations. We show that Formal Concept Analysis structures and algorithms are well-suited to this problem.(c) 2021 Elsevier Inc. All rights reserved.

引用

页码：338 / 348

页数：11

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