Post-processing a classifier's predictions: Strategies and empirical evaluation

被引：0

作者：

Benferhat, Salem ^{[1
]}

Tabia, Karim ^{[1
]}

Kezih, Mouaad ^{[2
]}

Taibi, Mahmoud ^{[2
]}

机构：

[1] Artois Univ, CRIL CNRS UMR 8188, Arras, France

[2] Badji Mokhtar Univ Annaba, Algiers, Algeria

来源：

21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014) | 2014年 / 263卷

关键词：

D O I：

10.3233/978-1-61499-419-0-965

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose an approach allowing to revise the outputs of a classifier in order to take into account the available domain knowledge. This approach can be applied for any classifier be it probabilistic or not. We propose post-processing criteria and methods to encode and exploit different kinds of domain knowledge. Finally, we provide experimental studies on a set of benchmarks.

引用

页码：965 / +

页数：2

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