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Copying Machine Learning Classifiers
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DOI：10.1109/ACCESS.2020.3020638.png)
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Abstract
En 中文
We study copying of machine learning classifiers, an agnostic technique to replicate the decision behavior of any classifier. We develop the theory behind the problem of copying, highlighting its properties, and propose a framework to copy the decision behavior of any classifier using no prior knowledge of its parameters or training data distribution. We validate this framework through extensive experiments using data from a series of well-known problems. To further validate this concept, we use three different use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics.
Keywords:
Training data
Machine learning
Data models
Training
Buildings
Production
Predictive models
Applied machine learning
classification
copying
differential replication
fidelity
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