An Integrated Process for Verifying Deep Learning Classifiers Using Dataset Dissimilarity Measures

Darryl Hond, Hamid Asgari, Daniel Jeffery, Mike Newman

OAI: oai:igi-global.com:289536 • DOI: 10.4018/IJAIML.289536

The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process for verifying artificial neural network (ANN) classifiers. This process consists of an off-line verification and an on-line performance prediction phase. The process is intended to verify ANN classifier generalisation performance, and to this end makes use of dataset dissimilarity measures. We introduce a novel measure for quantifying the dissimilarity between the dataset used to train a classification algorithm, and the test dataset used to evaluate and verify classifier performance. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and a dissimilarity measure; such a requirement could be verified by dynamic testing. Experimental results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.

Computer Science and Information Technology Artificial Intelligence Machine Learning