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

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

Joint-rollout of FTTH and smart city fiber networks as a way to reduce rollout cost

Making cities smarter is the future. By bringing more technology into existing city infrastructure, smart city applications can arise. Whether these applications track devices e.g. public lightning, environmental measurements e.g. temperature or air quality, or analyze video streams e.g. for people density, it is expected that these will require a (near-) real time data connection. Upcoming 5G networks will be able to handle large amounts of connections at high speeds and low latencies and will therefor outperform current technologies such as 4G and low-power wide-area networks. In order to do so, these 5G networks fall back to numerous fiber connected small cells for up & downlink to the Internet. In this publication, we are looking into the additional fiber equipment and deployment cost to connect the required smart city network infrastructure, taking into account a Fiber-to-the-Home (FTTH) network is already available or will be installed as part of the smart city network rollout. More concretely, we are proposing a methodology comparing an anticipated and incremental planning approach for a number of different extensions upon the FTTH-network: connecting all electrical cabinets, connecting public lightning, and the connection of 5G using small cells. From this, we want to learn how much the total rollout cost can be reduced using a future-oriented smart city approach taking into account all future extensions, compared to an incremental short-time planning only planning additional fiber when required. In the meantime, we want to show the additional cost of creating a smart city network is limited when it is being combined with a FTTH rollout. Results of the proposed methodology and use case will be modeled planning and design software Comsof Fiber and will be published in a future work

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

Deep learning for infrared thermal image based machine health monitoring

The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the subfield of feature learning, i.e., deep learning (DL), more specifically convolutional neural networks (NNs), is researched in this paper. The objective of this paper is to investigate if and how DL can be applied to infrared thermal (IRT) video to automatically determine the condition of the machine. By applying this method on IRT data in two use cases, i.e., machinefault detection and oil-level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e., 95 and 91.67% accuracy for the respective use cases), without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained NNs, important regions in the IRT images can be identified related to specific conditions, which can potentially lead to new physical insights