Decision trees: from efficient prediction to responsible AI

被引：33

作者：

Blockeel, Hendrik ^{[1
,2
]}

Devos, Laurens ^{[1
,2
]}

Frenay, Benoit ^{[3
]}

Nanfack, Geraldin ^{[3
]}

Nijssen, Siegfried ^{[4
]}

机构：

[1] Katholieke Univ Leuven, Dept Comp Sci, Leuven, Belgium

[2] Katholieke Univ Leuven, Inst Artificial Intelligence Leuven AI, Leuven, Belgium

[3] Univ Namur, Fac Comp Sci, Namur, Belgium

[4] UCLouvain, ICTEAM, Ottignies Louvain La Neuv, Belgium

来源：

FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2023年 / 6卷

关键词：

decision trees; ensembles; responsible AI; machine learning; learning under constraints; explainable AI; combinatorial optimization; FORMAL VERIFICATION; INDUCTION; FORESTS; CLASSIFICATION; REGRESSION; CONSTRUCTION; ENSEMBLES; FRAMEWORK;

D O I：

10.3389/frai.2023.1124553

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and weaknesses of decision trees in this context. The main goal of the article is to clarify the broad relevance to machine learning and artificial intelligence, both practical and theoretical, that decision trees still have today.

引用

页数：17

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