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arXiv 2022年

作者： Almirall, Esteve Callegaro, Davide Bruins, Peter Santamaría, Mar Martrínez, Pablo Cortés, Ulises Esade Business School URL University Spain 300.000km *** Spain Knowledge Engineering and Machine Learning Group Universitat Politècnica de Catalunya Spain

The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, is needed. But experimentation in cities has many undesirable effects not only for its citizens but also reputational if unsuccessful. Digital Twins, so popular in other areas, seem like a promising way to expand experimentation proposals but in simulated environments, translating only the "half-baked" ones, the ones with higher probability of success, to real environments and therefore minimizing risks. However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection. We present an alternative based on synthetic data that given some conditions, quite common in Smart Cities, can solve these two problems together with a proof-of-concept based on NO2 pollution. © 2022, CC BY.

关键词： Smart city

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Automatic interpretation of classes for improving decision support

Automatic interpretation of classes for improving decision s...

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5th Biennial Conference of the International Environmental Modelling and Software Society: Modelling for Environment's Sake, iEMSs 2010

作者： Gibert, K. Pérez-Bonilla, A. Knowledge Engineering and Machine Learning Group Universitat Politècnica de Catalunya Edif. C5 Jordi Girona 1-3 08034 Barcelona Spain Department of Statistics and Operations Research Universitat Politècnica de Catalunya Edif. C5 Jordi Girona 1-3 08034 Barcelona Spain

ISBN: (纸本)9788890357411

More and more, the analysis of clustering results becomes difficult as the number of variables considered increases, and the number of classes is not low. Sometimes concept induction methods are used to associate concepts to every class and use to be expressed as boolean expressions, easy to understand and supposedly providing good support to decision making. It has been seen that most of the concept induction algorithms priorize the compacity of the final expressions, as well as their predictive power. However, for descriptive purposes, when the meaning of classes has to be recognized and understood by the expert, this is not the best approach, since compacity directly implies elimination of redundancies or strong associations, while comprehension of the class is mainly based in understanding how variables interact among them inside the class. Here a method to induce conceptual descriptions of classes is proposed, providing non-minimal descriptions of the classes, but richer ones including the characteristics that distinguishes a class from the others, in such a way that expert can easily recognize the essence of the class, and conceptualize it on the bases of local interactions among all variables observed inside every class. This kind of interpretations provide an excellent support for later decision support systems.

关键词： Decision support systems

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knowledge discovery on the response to neurorehabilitation treatment of patients with Traumatic Brain Injury through an AI&Stats and graphical hybrid methodology

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作者： Gibert, Karina García-Rudolph, Alejandro García-Molina, Alberto Roig-Rovira, Teresa Bernabeu, Montserrat María Tormos, José Statistics and Operation Research Department Universitat Politècnica de Catalunya Campus Nord C Jordi Girona 1-3 08034 Barcelona Spain Knowledge Engineering and Machine Learning Group Universitat Politècnica de Catalunya Barcelona Spain Institut Guttmann-Hospital de Neurorehabilitació Badalona Spain

ISBN: (纸本)9781586039257

Neuropsychological rehabilitation seeks to reduce cognitive disability after acquired brain injury. However, until now, there is not enough data to allow exercise of neuropsychological rehabilitation based on scientific Class I evidence. From the medical point of view, the purpose of this work is to develop a classificatory tool to identify different populations of patients based on the characteristics of deficit and response to treatment. This knowledge Discovery problem has been faced by using exogenous clustering based on rules, an hybrid AI and Statistics technique, which combines some Inductive learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of typical profiles. In this paper, the results of applying this technique to a sample of patients with Traumatic Brain Injury are presented and their advantages with regards to other more classical analysis approaches are discussed. © 2008 The authors and IOS Press. All rights reserved.

关键词： knowledge management

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A Framework for the Development and Maintenance of Adaptive, Dynamic, Context-Aware Information Services

A Framework for the Development and Maintenance of Adaptive,...

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2nd International Conference on Agents and Artificial Intelligence, ICAART 2010

作者： Palau, Manel Gómez-Sebastià, Ignasi Ceccaroni, Luigi Vázquez-Salceda, Javier Nieves, Juan Carlos TMT Factory C/ Moià 1 planta 1 E08006 Barcelona Spain Knowledge Engineering and Machine Learning Group UPC C/Jordi Girona 1-3 E08034 Barcelona Spain Barcelona Digital Centre Tecnològic C/Roc Boronat 117 5a planta E08018 Barcelona Spain

ISBN: (纸本)9783642198892

This paper presents an agent-based methodological approach to design distributed service-oriented systems which can adapt their behaviour according to changes in the environment and in the user needs, even taking the initiative to make suggestions and proactive choices. The highly dynamic, regulated, complex nature of the distributed, interconnected services is tackled through a methodological framework composed of three interconnected levels. The framework relies on coordination and organisational techniques, as well as on semantically annotated Web services to design, deploy and maintain a distributed system, using both a top-down and bottom-up approach. We present results based on a real use case: interactive community displays with tourist information and services, dynamically personalised according to user context and preferences. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Multi agent systems

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Decreasing uncertainty when interpreting profiles through the traffic lights panel

Decreasing uncertainty when interpreting profiles through th...

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14th International Conference on Information Processing and Management of Uncertainty in knowledge-Based Systems, IPMU 2012

作者： Gibert, Karina Conti, Dante Sànchez-Marrè, Miquel Dep. of Statistics and Operation Research Universitat Politècnica de Catalunya Barcelona Spain Knowledge Engineering and Machine Learning Group Universitat Politècnica de Catalunya Barcelona Tech. Spain Dep. of Operations Research Universidad de Los Andes Mérida Venezuela Dep. Software Universitat Politècnica de Catalunya Spain

ISBN: (纸本)9783642317149

This paper describes Traffic Lights Panel (TLP) as a useful interpretation-oriented tool for clustering results, suitable for helping the domain experts to induce a conceptualization of the resulting profiles. Till now, the TLP is manually derived from the clustering results, but it has been well accepted by the domain experts of several real applications as a very helpful contribution to understand the classes' meaning and improve reliable decision-making. Here, a proposal to automatically construction of TLP is presented trying to mimic the real process that the analyst performs. Two criteria based on different central trend statistics of the variables inside a class are introduced, tested with a real case study in Neurorehabilitation field and compared. Finally, uncertainty concerning TLP is analyzed;the annotated TLP (aTLP) is proposed to visualize uncertainty associated to the decisions derived from TLP, thus enhancing robustness of TLP as a supporting tool in decision-making. © 2012 Springer-Verlag.

关键词： Decision making

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ATLP: A color-based model of uncertainty to evaluate the risk of decisions based on prototypes

ATLP: A color-based model of uncertainty to evaluate the ris...

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作者： Gibert, Karina Conti, Dante Knowledge Engineering and Machine Learning group Department Statistics and Operation Research Universitat Politècnica de Catalunya-BarcelonaTech Barcelona Spain Department of Operations Research Universidad de Los Andes Mérida Venezuela Instituto de Matemática Estadistica e Computacao Cientifica Departamento de Matemática Aplicada U. Estadual de Campinas Sao Paulo Brazil

Clustering techniques find homogeneous and distinguishable prototypes. Careful interpretation of these prototypes is crucial to assist the experts to better organize this know-how and to really improve their decision-making processes. The Traffic Lights Panel was introduced in 2009 as a postprocessing tool to provide understanding of clustering prototypes. In this work, annotated Traffic Lights Panel (aTLP) is presented as an enrichment of the TLP to manage the intrinsic uncertainty related with prototypes themselves. The aTLP handles uncertainty through a quantification of the prototypes' purity based on the variation coefficients (VC) and an associated color-based uncertainty model, with two dimensions-tone and saturation-representing nominal trend and purity of the prototype. An application to a waste-water treatment plant in Slovenia, in a discrete and continuous approach, suggests that aTLP seems a useful and friendly tool able to reduce the gap between data mining and effective decision support, towards informed-decisions. © 2015 - IOS Press and the authors.

关键词： Decision making

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On the role of pre and post-processing in environmental data mining

On the role of pre and post-processing in environmental data...

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4th Biennial Meeting of International Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008

作者： Gibert, Karina Izquierdo, Joaquín Holmes, Geoff Athanasiadis, Ioannis Comas, Joaquim Sànchez-Marrè, Miquel Department of Statistics and Operation Research Technical University of Catalonia Barcelona Catalonia Spain Knowledge Engineering and Machine Learning Group Technical University of Catalonia Barcelona Catalonia Spain Centro Multidisciplinar de Modelación de Fluidos Universidad Politécnica de Valencia Spain Department of Computer Science University of Waikato New Zealand Istituto Dalle Molle di Studi sull'Intelligenza Artificiale Lugano Switzerland University of Girona Spain

ISBN: (纸本)9788476530740

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of knowledge Discovery in environmental systems is discussed.

关键词： Data mining

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Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

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arXiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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Data mining as a tool for environmental scientists

Data mining as a tool for environmental scientists

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3rd Biennial Meeting of the International Environmental Modelling and Software Society: Summit on Environmental Modelling and Software, iEMSs 2006

作者： Spate, Jessica Gibert, Karina Sànchez-Marrè, Miquel Frank, Eibe Comas, Joaquim Athanasiadis, Ioannis Letcher, Rebecca Mathematical Sciences Institute Australian National University Canberra Australia Department of Statistics and Operation Research Technical University of Catalonia Barcelona Catalonia Spain Knowledge Engineering and Machine Learning Group Technical University of Catalonia Barcelona Catalonia Spain Department of Computer Science University of Waikato Waikato New Zealand University of Girona Girona Catalonia Spain Istituto Dalle Molle di Studi Sull'Intelligenza Artificiale Lugano Switzerland Integrated Catchment Assessment and Management Centre Australian National University Canberra Australia

ISBN: (纸本)1424308526

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous.

关键词： Data mining

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Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research

arXiv

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arXiv 2024年

作者： Hille, Tobias Stubbemann, Maximilian Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Institute of Computer Science University of Hildesheim Hildesheim Germany

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained *** Codes 68T01 68T07 68T09 51F99 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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