GENESIM : genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. To bridge this gap, we present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm. We compared GENESIM to prevalent decision tree induction and ensemble techniques using twelve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex System

The young Van Dyck’s fingerprint : a technical approach to assess the authenticity of a disputed painting

The painting Saint Jerome, part of the collection of the Maagdenhuis Museum (Antwerp, Belgium), is attributed to the young Anthony van Dyck (1613–1621) with reservations. The painting displays remarkable compositional and iconographic similarities with two early Van Dyck works (1618–1620) now in Museum Boijmans van Beuningen (Rotterdam) and Nationalmuseum (Stockholm). Despite these similarities, previous art historical research did not result in a clear attribution to this master. In this study, the work’s authenticity as a young Van Dyck painting was assessed from a technical perspective by employing a twofold approach. First, technical information on Van Dyck’s materials and techniques, here identified as his fingerprint, were defined based on a literature review. Second, the materials and techniques of the questioned Saint Jerome painting were characterized by using complementary imaging techniques: infrared reflectography, X-ray radiography and macro X-ray fluorescence scanning. The insights from this non-invasive research were supplemented with analysis of a limited number of cross-sections by means of field emission scanning electron microscopy coupled with energy dispersive X-ray spectroscopy. The results demonstrated that the questioned painting’s materials and techniques deviate from Van Dyck’s fingerprint, thus making the authorship of this master very unlikely

Interpretable detection of unstable smart TV usage from power state logs

Power state logs from smart TVs are collected in order to construct a time-series representation of their usage. Time-series that belong to a TV exhibiting instability problems are classified accordingly. To do so, an automated feature extraction approach is used, together with linear classification methods in order to realise interpretable classification decisions. A normalized true positive rate of 0.84 ± 0.10 is obtained for the classification. The normalized true negative rate equals 0.80 ± 0.03. The final model returns a regularity statistic called the Approximate Entropy as its most important feature

Trends in global CO2 emissions: 2012 report

Global emissions of carbon dioxide (CO2) – the main cause of global warming – increased by 3% in 2011, reaching an all-time high of 34 billion tonnes in 2011. In 2011, China’s average per capita carbon dioxide (CO2) emissions increased by 9% to 7.2 tonnes CO2¬, whereas these emissions in the European Union declined by 4 % to 7.5 tonnes CO2, bringing for the first time Europe’s and China’s CO2 emissions on similar levels. China, the world’s most populous country, is now well within the 6 to 19 tonnes/person range spanned by the major industrialised countries. In comparison, in 2011, the United States was still one of the largest emitters of CO2, with 16.5 tonnes in per capita emissions, after a steep decline mainly caused by the recession in 2008-2009, high oil prices compared to low fuel taxes and an increased share of natural gas. This is one of the main findings of the annual report ‘Trends in global CO2 emissions’, released today by PBL Netherlands Environmental Assessment Agency and the European Commission’s Joint Research Centre (JRC).JRC.H.2-Air and Climat

Thermal imaging and vibration-based multisensor fault detection for rotating machinery

In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early and automatically. To enable this, sensor streams should continuously be monitored, processed, and interpreted. In recent years, infrared thermal imaging has gained attention for the said purpose. However, the detection capabilities of a system that uses infrared thermal imaging is limited by the modality captured by this single sensor, as is any single sensor-based system. Hence, within this paper a multisensor system is proposed that not only uses infrared thermal imaging data, but also vibration measurements for automatic condition and fault detection in rotating machinery. It is shown that by combining these two types of sensor data, several conditions/faults and combinations can be detected more accurately than when considering the sensor streams individually

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often analyzing several types of medical images generated by different types of medical equipment. Capturing and analyzing these medical images is labor-intensive and expensive. In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques. Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. Quantitative and qualitative results, as obtained for a small-sized dataset with no segmentation ground truth, demonstrate that the proposed approach is promising, for instance achieving an accuracy of 0.91$\pm0.02$ and an ROC-AUC score of 0.94 for the diagnosis task. Furthermore, we present a publicly available prototype web application that integrates our predictive model, with the goal of making effective glaucoma diagnosis available to a wide audience.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/010120

A generalized matrix profile framework with support for contextual series analysis

The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generalizing the approach. We derived a framework that focuses on the implicit distance matrix calculation. We present this framework as the Series Distance Matrix (SDM). In this framework, distance measures (SDM-generators) and distance processors (SDM-consumers) can be freely combined, allowing for more flexibility and easier experimentation. In SDM, the Matrix Profile is but one specific configuration. We also introduce the Contextual Matrix Profile (CMP) as a new SDM-consumer capable of discovering repeating patterns. The CMP provides intuitive visualizations for data analysis and can find anomalies that are not discords. We demonstrate this using two real world cases. The CMP is the first of a wide variety of new techniques for series analysis that fits within SDM and can complement the Matrix Profile