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arXiv 2018年

作者： Cano, José-Ramón Gutiérrez, Pedro Antonio Krawczyk, Bartosz Woźniak, Michal García, Salvador Dept. of Computer Science University of Jaén EPS of Linares Avenida de la Universidad S/N Linares Jaén23700 Spain Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Computer Science Virginia Commonwealth University RichmondVA23284 United States Department of Computer Science Wroclaw University of Technology Wyb. Wyspiańskiego 27 Wroclaw50-370 Poland Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview about the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining

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A Bidirectional Subsethood Based Similarity Measure for Fuzzy Sets

A Bidirectional Subsethood Based Similarity Measure for Fuzz...

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IEEE International Conference on Fuzzy Systems

作者： Shaily Kabir Christian Wagner Timothy C. Havens Derek T. Anderson Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID) University of Nottingham Nottingham UK Dept. Electrical and Computer Engineering Michigan Technological University Houghton MI USA Dept. Electrical Engineering and Computer Science University of Missouri Columbia MO USA

Similarity measures are useful for reasoning about fuzzy sets. Hence, many classical set-theoretic similarity measures have been extended for comparing fuzzy sets. In previous work, a set-theoretic similarity measure considering the bidirectional subsethood for intervals was introduced. The measure addressed specific concerns of many common similarity measures, and it was shown to be bounded above and below by Jaccard and Dice measures respectively. Herein, we extend our prior measure from similarity on intervals to fuzzy sets. Specifically, we propose a vertical-slice extension where two fuzzy sets are compared based on their membership values. We show that the proposed extension maintains all common properties (i.e., reflexivity, symmetry, transitivity, and overlapping) of the original fuzzy similarity measure. We demonstrate and contrast its behaviour along with common fuzzy set-theoretic measures using different types of fuzzy sets (i.e., normal, non-normal, convex, and non-convex) in respect to different discretization levels.

关键词： Fuzzy sets Current measurement computer science Atmospheric measurements Particle measurements Conferences Fuzzy systems

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Analytic solutions for stochastic hybrid models of gene regulatory networks

arXiv

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arXiv 2018年

作者： Kurasov, Pavel Mugnolo, Delio Wolf, Verena Dept. of Mathematics Stockholm Univ. Stockholm106 91 Sweden Chair of Analysis University of Hagen Hagen58084 Germany Computer Science Department Saarland University Saarbrücken66123 Germany

Discrete-state stochastic models are a popular approach to describe the inherent stochasticity of gene expression in single cells. The analysis of such models is hindered by the fact that the underlying discrete state space is extremely large. Therefore hybrid models, in which protein counts are replaced by average protein concentrations, have become a popular alternative. The evolution of the corresponding probability density functions is given by a coupled system of hyperbolic PDEs. This system has Markovian nature but its hyperbolic structure makes it difficult to apply standard functional analytical methods. We are able to prove convergence towards the stationary solution and determine such equilibrium explicitly by combining abstract methods from the theory of positive operators and elementary ideas from potential *** Codes 35B09, 47D06, 93C20, 35F46 Copyright © 2018, The Authors. All rights reserved.

关键词： Gene expression

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On the effect of local search in the multi-objective evolutionary discovery of software architectures

On the effect of local search in the multi-objective evoluti...

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Congress on Evolutionary Computation

作者： Aurora Ramírez José Raúl Romero Sebastián Ventura Universidad de Cordoba Cordoba AndalucÃa ES Dept. of Computer Science and Numerical Analysis University of Córdoba Campus of Rabanales Córdoba Spain

ISBN: (纸本)9781509046027

Software architects devote substantial efforts to find the most fitting architectural description for their system, which should not only specify its structure, but is also required to meet multiple, simultaneous quality criteria. Evolutionary computation has recently demonstrated to provide insightful support during the design phase by automatically deciding how to organise internal software components and how they should interact each other. Observed from a multi-objective perspective, particular care has to be taken in order to reach an appropriate trade-off among design metrics, while providing the software engineer with diverse alternatives to choose among. However, multi-objective evolutionary algorithms may find difficulties to control both aspects and, at the same time, to explore the entire search space in dept.. Under these circumstances, local search can be applied to complement the evolution by scrutinising the most promising search directions. This paper proposes two different approaches that take advantage of the benefits of local search within the multi-objective evolutionary discovery of component-based software architectures. A detailed analysis and comparative study provides interesting findings like the importance of assigning a sufficient number of evaluations to the local improvement. The way in which local search explores and compares solutions for acceptance is a relevant aspect to promote diversity during the discovery process as well.

关键词： Software architecture Search problems computer architecture Memetics Evolutionary computation Measurement Mathematical model

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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

arXiv

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arXiv 2018年

作者： Cheplygina, Veronika De Bruijne, Marleen Pluim, Josien P.W. Biomedical Imaging Group Rotterdam Depts. Radiology and Medical Informatics Erasmus Medical Center Rotterdam Medical Image Analysis Dept. Biomedical Engineering Eindhoven University of Technology Eindhoven Image Sciences Institute University Medical Center Utrecht Utrecht Image Section Dept. Computer Science University of Copenhagen Copenhagen Denmark

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical imaging

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Machine learning paradigms for weed mapping via unmanned aerial vehicles

Machine learning paradigms for weed mapping via unmanned aer...

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2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

作者： Perez-Ortiz, M. Gutierrez, P.A. Pena, J.M. Torres-Sanchez, J. Lopez-Granados, F. Hervas-Martinez, C. Dept. of Quantitative Methods Universidad Loyola Andalucía Córdoba14004 Spain Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain Institute for Sustainable Agriculture CSIC Córdoba14080 Spain

ISBN: (纸本)9781509042401

This paper presents a novel strategy for weed monitoring, using images taken with unmanned aerial vehicles (UAVs) and concepts of image analysis and machine learning. Weed control in precision agriculture designs site-specific treatments based on the coverage of weeds, where the key is to provide precise weed maps timely. Most traditional remote platforms, e.g. piloted planes or satellites, are, however, not suitable for early weed monitoring, given their low temporal and spatial resolutions, as opposed to he ultra-high spatial resolution of UAVs. The system here proposed makes use of UAV-imagery and is based on: 1) Divide the image, 2) compute and binarise the vegetation indexes, 3) detect crop rows, 4) optimise the parameters and 4) learn a classification model. Since crops are usually organised in rows, the use of a crop row detection algorithm helps to separate properly weed and crop pixels, which is a common handicap given the spectral similitude of both. Several artificial intelligence paradigms are compared in this paper to identify the most suitable strategy for this topic (i.e. unsupervised, supervised and semi-supervised approaches). Our experiments also study the effect of different parameteres: the flight altitude, the sensor and the use of previously trained models at a different height. Our results show that 1) very promising performance can be obtained, even when using very few labelled data and 2) the classification model can be learnt in a subplot of the experimental field at low altitude and then applied to the whole field at a higher height, which simplifies the whole process. These results motivate the use of this strategy to design weed monitoring strategies for early post-emergence weed control. © 2016 IEEE.

关键词： Weed control

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Management and control of stochastic drug supply chain by KANBAN and Petri Net 3

Management and control of stochastic drug supply chain by KA...

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3rd IEEE International Conference on Logistics Operations Management, GOL 2016

作者： Jebbor, Sara Afia, Abdellatif El Chiheb, Raddouane Ouzayd, Fatima Dept. Logistical and Operational Research National Superior School of Computer Science and Systems Analysis Mohammed v University in Rabat Rabat Morocco

ISBN: (纸本)9781467385718

This paper aims to improve the management of internal drug logistic flows and both, reduce and master the different stochastic aspects in order to increase the degree of flexibility and robustness of drug supply chain. To achieve this goal, we propose as the first solution the KANBAN system implementation with a priority management policy, as an effective management system for hospital supply and inventory. The second proposition is a flexible structure implementation to reduce and master the stochastic effect coming from suppliers. Finally, the Colored & Timed Petri Net is used to model drug logistic flows with the two above propositions, and will serve as a control and decision-support tool for drug supply chain. © 2016 IEEE.

关键词： Supply chains

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DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

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arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

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Adapting Linear Discriminant analysis to the Paradigm of Learning from Label Proportions

Adapting Linear Discriminant Analysis to the Paradigm of Lea...

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IEEE Symposium Series on Computational Intelligence

作者： M. Pérez-Ortiz P.A. Gutiérrez M. Carbonero-Ruz C. Hervás-Martínez Dept. of Quantitative Methods Universidad Loyola Andalucía 14004 Córdoba Spain Dept. of Computer Science and Numerical Analysis University of Córdoba 14071 Spain

ISBN: (纸本)9781509042418

The recently coined term "learning from label proportions" refers to a new learning paradigm where training data is given by groups (also denoted as "bags"), and the only known information is the label proportion of each bag. The aim is then to construct a classification model to predict the class label of an individual instance, which differentiates this paradigm from the one of multi-instance learning. This learning setting presents very different applications in political science, marketing, healthcare and, in general, all fields in relation with anonymous data. In this paper, two new strategies are proposed to tackle this kind of problems. Both proposals are based on the optimisation of pattern class memberships using the data distribution in each bag and the known label proportions. To do so, linear discriminant analysis has been reformulated to work with non-crisp class memberships. The experimental part of this paper sets different objetives: 1) study the difference in performance, comparing our proposals and the fully supervised setting, 2) analyse the potential benefits of refining class memberships by the proposed approaches, and 3) test the influence of other factors in the performance, such as the number of classes or the bag size. The results of these experiments are promising, but further research should be encouraged for studying more complex data configurations.

关键词： weak supervision linear discriminant analysis learning from label proportions

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Representing ordinal input variables in the context of ordinal classification

Representing ordinal input variables in the context of ordin...

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International Joint Conference on Neural Networks

作者： P. A. Gutierrez M. Perez-Ortiz J. Sanchez-Monedero C. Hervas-Martinez Dept. of Computer Science and Numerical Analysis Universidad de Córdoba 14071 Spain Dept. of Quantitative Methods Universidad Loyola Andalucía 14004 Córdoba Spain

ISBN: (纸本)9781509006212

Ordinal input variables are common in many supervised and unsupervised machine learning problems. We focus on ordinal classification problems, where the target variable is also categorical and ordinal. In order to represent categorical input variables for measuring distances or applying continuous mapping functions, they have to be transformed to numeric values. This paper evaluates five different methods to do so. Two of them are commonly applied by practitioners, the first one based on binarising the ordinal input variable using standard indicator variables (NomBin), and the second one based on directly mapping each category to a consecutive natural number (Num). Furthermore, three novel proposals are evaluated in this paper: 1) an ordinal binarisation based on considering the order of the input variable (OrdBin), 2) the analysis of pairwise distances between input patterns to recover the latent variable generating the ordinal one (NumLVR), and 3) the refinement of the standard numeric transformation by recovering the distance between sets of patterns of consecutive categories (NumCDR). A thorough empirical evaluation is made, considering 12 datasets, 5 performance metrics and 4 classifiers (2 of them of nominal nature and 2 of ordinal nature). The results show that the Nom-Bin representation method leads to the worst results, and that both Num and NumCDR methods obtain very good performance, although NumCDR results are consistently better for almost all performance metrics and classifiers considered.

关键词： Input variables Encoding Standards Training Electronic mail Measurement Context

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