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Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

This article is concerned with sample size determination methodology for prediction models. We propose to combine the individual calculations via learning‐type curves. We suggest two distinct ways of doing so, a deterministic skeleton of a learning curve and a Gaussian process centered upon its deterministic counterpart. We employ several learning algorithms for modeling the primary endpoint and distinct measures for trial efficacy. We find that the performance may vary with the sample size, but borrowing information across sample size universally improves the performance of such calculations. The Gaussian process‐based learning curve appears more robust and statistically efficient, while computational efficiency is comparable. We suggest that anchoring against historical evidence when extrapolating sample sizes should be adopted when such data are available. The methods are illustrated on binary and survival endpoints. [ABSTRACT FROM AUTHOR]

We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models. [ABSTRACT FROM AUTHOR]

We present six datasets containing telemetry data of the Mars Express Spacecraft (MEX), a spacecraft orbiting Mars operated by the European Space Agency. The data consisting of context data and thermal power consumption measurements, capture the status of the spacecraft over three Martian years, sampled at six different time resolutions that range from 1 min to 60 min. From a data analysis point-of-view, these data are challenging even for the more sophisticated state-of-the-art artificial intelligence methods. In particular, given the heterogeneity, complexity, and magnitude of the data, they can be employed in a variety of scenarios and analyzed through the prism of different machine learning tasks, such as multi-target regression, learning from data streams, anomaly detection, clustering, etc. Analyzing MEX's telemetry data is critical for aiding very important decisions regarding the spacecraft's status and operation, extracting novel knowledge, and monitoring the spacecraft's health, but the data can also be used to benchmark artificial intelligence methods designed for a variety of tasks. Measurement(s) electric current Technology Type(s) current readings in spacecraft housekeeping telemetry Sample Characteristic - Environment outer space [ABSTRACT FROM AUTHOR]

International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyze such data, several machine learning, bioinformatics, and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyze multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis. [ABSTRACT FROM AUTHOR]

The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its predefined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The remaining power can then be allocated for scientific purposes. We present a machine learning pipeline for efficiently constructing accurate predictive models for predicting the power of the thermal subsystem on board MEX. In particular, we employ state-of-the-art feature engineering approaches for transforming raw telemetry data, in turn used for constructing accurate models with different state-of-the-art machine learning methods. We show that the proposed pipeline considerably improve our previous (competition-winning) work in terms of time efficiency and predictive performance. Moreover, while achieving superior predictive performance, the constructed models also provide important insight into the spacecraft's behavior, allowing for further analyses and optimal planning of MEX's operation. [ABSTRACT FROM AUTHOR]

Process-based modeling is an approach to learning understandable, explanatory models of dynamic systems from domain knowledge and data. Although their utility has been proven on many tasks of modeling dynamic systems in various domains, their ability to accurately predict the future behavior of an observed system is limited. To address this limitation, we propose the use of a standard approach to improving the predictive performance of machine learning methods, i.e., the approach of learning ensemble models. Previous work on ensembles of process-based models has been limited to proof-of-principle experiments with a single ensemble method (bagging) and in the limited perspective of explaining the currently observed system behavior v.s. predicting future system behavior. In this paper, we design a general methodology for adapting ensemble methods to the context of process-based modeling. Using the methodology, we implement the two approaches bagging and boosting of process-based models. We perform an empirical evaluation of the implemented methods on three real-world modeling problems from the domain of population dynamics in aquatic ecosystems. The results of the empirical evaluation show that ensembles of process-based models can lead to long-term predictions of the population dynamics that are more accurate than the ones obtained with a single process-based model. [ABSTRACT FROM AUTHOR]

Ensemble methods are machine learning methods that construct a set of models and combine their outputs into a single prediction. The models within an ensemble can have different structure and parameters and make diverse predictions. Ensembles achieve high predictive performance, benefiting from the diversity of the individual models and outperforming them. In this paper, we develop a novel method for learning ensembles of process-based models. We build upon existing approaches to learning process-based models of dynamic systems from observational data, which integrates the theoretical and empirical paradigms for modelling dynamic systems. In addition to observed data, process-based modelling takes into account domain-specific modelling knowledge. We apply the newly developed method and evaluate its utility on a set of problems of modelling population dynamics in aquatic ecosystems. Data on three lake ecosystems are used, together with a library of process-based knowledge on modelling population dynamics. Based on the evaluation results, we identify the optimal settings of the method for learning ensembles of process-based models, i.e., the optimal number of ensemble constituents (25) as well as the optimal way to select (using a separate validation set) and combine them (using simple average). Furthermore, the evaluation results show that ensemble models have significantly better predictive performance than single models. Finally, the ensembles of process-based models accurately simulate the current and predict the future behaviour of the three aquatic ecosystems. [ABSTRACT FROM AUTHOR]

We propose Fuzzy Jaccard Index (Fuji) — a scale-invariant score for similarity assessment of two ranked/ordered lists. Fuji improves upon the Jaccard index by incorporating a membership function that takes into account the particular ranks, thus producing both more stable and more accurate similarity estimates. We provide theoretical insights into the properties of the Fuji score as well as propose an efficient algorithm for computing it. We also present empirical evidence of its performance in different synthetic scenarios. Finally, we demonstrate its utility in a typical machine learning setting — comparing feature ranking lists, relevant to a given machine learning task. In many practical applications, in particular originating from high-dimensional domains, where only a small percentage of the whole feature space might be relevant, a robust and confident feature ranking leads to interpretable findings, efficient computation and good predictive performance. In such cases, Fuji correctly distinguishes between existing feature ranking approaches, while being more robust and efficient than the benchmark similarity scores. • A score (Fuzzy Jaccard Index (FUJI) for comparing ordered lists is proposed. • FUJI's theoretic properties are thoroughly investigated. • A synthetic study shows, how the limitations of other scores are resolved by FUJI. • An extensive empirical study shows the usefulness of FUJI in real-world scenarios. [ABSTRACT FROM AUTHOR]

INTEGRAL (INTErnational Gamma-Ray Astrophysics Laboratory) is an astronomical observatory of the European Space Agency, responsible for many significant scientific discoveries in the last few decades. It orbits Earth since 2002 in a highly elliptical orbit, passing through the Van Allen belts – areas with high-energy ionized particles that can damage the spacecraft's on-board equipment. An essential part of mission planning and operation of INTEGRAL is thus the prediction of its radiation belts entry and exit times. We propose a novel compact representation of the data and evaluate its potential using several machine learning methods. The experimental validation identifies gradient boosted trees with quantile loss as the best performing method. By using our approach, INTEGRAL can perform 2 additional hours (on average) of scientific measurements per orbit (with adjustment for uncertainty at the 95th percentile). This approach protects INTEGRAL from damages and improves its scientific return at the same time. It can be easily extended and applied to other spacecraft with similar orbits. [ABSTRACT FROM AUTHOR]