DocumentCode :
2485380
Title :
ExOpaque: A Framework to Explain Opaque Machine Learning Models Using Inductive Logic Programming
Author :
Guo, Yunsong ; Selman, Bart
Author_Institution :
Cornell Univ., Ithaca
Volume :
2
fYear :
2007
fDate :
29-31 Oct. 2007
Firstpage :
226
Abstract :
In this paper we developed an Inductive Logic Programming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning model, to describe the behavior of the opaque model with high fidelity while maintaining the simplicity of the Horn clauses for human interpretations.
Keywords :
Horn clauses; inductive logic programming; learning (artificial intelligence); set theory; ExOpaque-opaque machine learning model; Horn clauses; inductive logic programming; Artificial intelligence; Biological system modeling; Cancer; Decision trees; Humans; Logic programming; Machine learning; Magnetic heads; Support vector machines; USA Councils;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
ISSN :
1082-3409
Print_ISBN :
978-0-7695-3015-4
Type :
conf
DOI :
10.1109/ICTAI.2007.140
Filename :
4410384
Link To Document :
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