For environmental, sustainable economic and political reasons, recycling processes are becoming increasingly important, aiming at a much higher use of secondary raw materials. Currently, for the copper and aluminium industries, no method for the non-destructive online analysis of heterogeneous materials are available. The Prompt Gamma Neutron Activation Analysis (PGNAA) has the potential to overcome this challenge. A difficulty when using PGNAA for online classification arises from the small amount of noisy data, due to short-term measurements. In this case, classical evaluation methods using detailed peak by peak analysis fail. Therefore, we propose to view spectral data as probability distributions. Then, we can classify material using maximum log-likelihood with respect to kernel density estimation and use discrete sampling to optimize hyperparameters. For measurements of pure aluminium alloys we achieve near perfect classification of aluminium alloys under 0.25 second.

An article on machine learning interpretation appeared on O'Reilly's blog back in March, written by Patrick Hall, Wen Phan, and SriSatish Ambati, which outlined a number of methods beyond the usual go-to measures. By chance I happened back upon the article again over the weekend, and with a fresh read decided to share some of the ideas contained within. The article is a great (if lengthy) read, and recommend it to anyone who has the time. Part 1 includes approaches for seeing and understanding your data in the context of training and interpreting machine learning algorithms, Part 2 introduces techniques for combining linear models and machine learning algorithms for situations where interpretability is of paramount importance, and Part 3 describes approaches for understanding and validating the most complex types of predictive models. The deconstruction of the interpretability of each technique and group of techniques is the focus of the article, while this post is a summary of the techniques.