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ICM: An Intuitive Model Independent and Accurate Certainty Measure for Machine Learning

Attachments

Author: Waa, J. van der · Diggelen, J. van · Neerincx, M. · Raaijmakers, S.
Type:article
Date:2018
Source:Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018), 2, 314-321
Identifier: 873439
doi: doi:10.5220/0006542603140321
ISBN: 978-989-758-275-2
Keywords: Artificial Intelligence · Computational Intelligence · Evolutionary Computing · Industrial Applications of AI · Knowledge Discovery and Information Retrieval · Knowledge-Based Systems · Machine Learning · Soft Computing · Symbolic Systems · Uncertainty in AI

Abstract

End-users of machine learning-based systems benefit from measures that quantify the trustworthiness of the underlying models. Measures like accuracy provide for a general sense of model performance, but offer no detailed information on specific model outputs. Probabilistic outputs, on the other hand, express such details, but they are not available for all types of machine learning, and can be heavily influenced by bias and lack of representative training data. Further, they are often difficult to understand for non-experts. This study proposes an intuitive certainty measure (ICM) that produces an accurate estimate of how certain a machine learning model is for a specific output, based on errors it made in the past. It is designed to be easily explainable to non-experts and to act in a predictable, reproducible way. ICM was tested on four synthetic tasks solved by support vector machines, and a real-world task solved by a deep neural network. Our results show that ICM is both more accurate and intuitive than related approaches. Moreover, ICM is neutral with respect to the chosen machine learning model, making it widely applicable