On the Diversity of Machine Learning Models for System Reliability

TLDR: The reliability model for three-version machine learning architecture is constructed with a diversity measure defined as the intersection of error spaces in the sample space and a necessary condition under which three- version architecture achieves a higher system reliability than a single machine learning module is derived.

Abstract: The diversity of system components is one of the important contributing factors of reliable and secure software systems. In a software fault-tolerant system using diverse versions of software components, a component failure caused by defects or malicious attacks can be covered by other versions. Machine learning systems can also benefit from such a multi-version approach to improve the system reliability. Nevertheless, there are few studies addressing this issue. In this paper, we experimentally analyze how outputs of machine learning modules can be diversified by using different versions of machine learning algorithms, neural network architectures and perturbated input data. The experiments are conducted on image classification tasks of MNIST data set and Belgian Traffic Sign data set. Different neural network architectures, support vector machines and random forests are used for constructing diverse machine learning models. The diversity is characterized by the coverage of errors over the test samples. We observe that the different machine learning models have quite different error coverages that can be leveraged for system reliability design. Based on the experimental results, we construct the reliability model for three-version machine learning architecture with a diversity measure defined as the intersection of error spaces in the sample space. From the presented reliability model, we derive a necessary condition under which three-version architecture achieves a higher system reliability than a single machine learning module.