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arXiv 2019年

作者： Mortier, Thomas Wydmuch, Marek Dembczyński, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznań University of Technology Poland Intelligent Systems and Machine Learning Universität Paderborn Germany

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility scores. Two of these algorithms make use of structural information in the form of a class hierarchy, which is often available in prediction problems with many classes. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Combining Computer Vision and Word Processing to Classify Film Genres

Combining Computer Vision and Word Processing to Classify Fi...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Nikita Andriyanov Vitaly Dementev Data Analysis and Machine Learning Department Financial University under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Security Department Ulyanovsk State Technical University Ulyanovsk Russia

The paper investigates various deep learning approaches for classifying movies by genre. To prepare the initial dataset, a system for parsing textual information and images from the Internet Movie database, IMDB (https://***/) was implemented. Pandas (Python library) methods were applied to generate the dataset. Particular attention was paid to the preparation of target information and the choice of the learning loss function due to the fact that the problem was reduced to a multi-label classification. A number of transfer learning algorithms have been implemented for convolutional neural network models and transformer architectures when processing movie posters, as well as simple machine learning models and deep learning transformer-type models for text classification. The paper proposes a method for complexing extracted features from heterogeneous data in another neural network to improve classification accuracy. It was possible to achieve an increase in the average cosine distance for the test sample by 3% higher than when using standard models and about 7% in sense of intersection over union metric.

关键词： Measurement Deep learning Computer vision Computational modeling Computer architecture Motion pictures Transformers

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Comparison of MS Custom Vision Auto machine learning with Algorithms Implementation Methods

Comparison of MS Custom Vision Auto Machine Learning with Al...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Nikita Andriyanov Yulia Kamalova Vitaly Dementiev Data Analysis and Machine Learning Department Financial University Under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Protection Department Ulyanovsk State Technical University Ulyanovsk Russia

This study is devoted to the acquaintance, application, and implementation of computer vision models in the automated Microsoft Custom Vision service, as well as comparing the results of such models with the results of work personally assembled using the PyTorch/Tensorflow frameworks. The base models were represented by convolutional neural networks, which are applied to the image classification task. The study showed that the functionality of the Custom Vision service, although not great, but they represent really strong models, because without searching for the architecture, tuning the hyperparameters of the convolutional neural network, it is not possible to achieve results comparable to automatic machine learning solutions. However, the disadvantages of such models are the complexity of their deployment. As data for the study, we used databases of images of pollen grains of various plants, as well as aviation security images recorded by introscopes at airports. The experiment showed that in terms of precision and recall metrics, the Custom Vision solution provides an advantage of about 2-3% on balanced data and up to 15% on unbalanced ones. Thus, it is shown that using machine learning cloud services, you can get the primary baseline model, which programmers can further develop and improve, and other users can try to launch in production without knowledge of machine and deep learning technologies if the model quality estimation is quite high.

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Fuzzy Logical-Linguistic analysis of data Quality in Financial Information Systems

Fuzzy Logical-Linguistic Analysis of Data Quality in Financi...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Lomazov Vadim Lomazov Vladislav Anichin David Petrosov Department of Applied Informatics and Mathematics Belgorod State Agricultural University Belgorod Russia Department of Economics Belgorod State Agricultural University Belgorod Russia Department of Data Analysis and Machine Learning Financial University Moscow Russia

The proposed approach to the analysis of data quality is based on the use of the apparatus of fuzzy logical-linguistic analysis by L. Zadeh. The indicators are presented in the form of linguistic variables, the semantics of which are set by experts. The constructed hierarchy of indicators makes it possible to increase the scientific validity of decisions while ensuring the quality of data in financial systems.

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance

Application of the Semantic Segmentation Method in the Asses...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Inna Tulokhonova Svetlana Mikhaylova Soelma Danilova Department of Software Engineering and Artificial Intelligence East Siberia State University of Technology and Management Ulan-Ude Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition.

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Blessemflood21: Advancing Flood analysis with a High-Resolution Georeferenced dataset for Humanitarian Aid Support

Blessemflood21: Advancing Flood Analysis with a High-Resolut...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop Sensors Floods Task analysis

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix

Method for Finding an Investment Strategy in the Case of a S...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

关键词： Large-scale systems Sparse matrices Risk management Covariance matrices Stock markets Portfolios Investment

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Investor Risk Profile Determination Model

Investor Risk Profile Determination Model

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market.

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Dynamic Approach to the analysis of Financial Structure: Overcoming the Bank-Based vs Market-Based Dichotomy

Dynamic Approach to the Analysis of Financial Structure: Ove...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Konstantin Krinichansky Natalia Grineva Department of Financial Markets and Financial Engineering Institute of Financial Studies Financial University Under the Government of the Russian Federation Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

A conceptual approach to the typology of countries' financial development has been developed, which will allow government authorities to make more accurate macroeconomic forecasts and apply a strategic approach to economic development planning. Modeling was conducted to analyze the impact of macroeconomic indicators, dummy variables of economic crises, and the technological component variable on indicators of financial development and variables characterizing the structure of the financial system.

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Modeling the dielectric constant of silicon-based nanocomposites using machine learning

Modeling the dielectric constant of silicon-based nanocompos...

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2020 International Conference on Actual Problems of Electron Devices Engineering, APEDE 2020

作者： Korchagin, Sergey Alekseevich Klinaev, Yuri Vasilievich Terin, Denis Vladimirovich Romanchuk, Sergey Petrovich Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Yuri Gagarin Saratov State Technical University Department of Natural and Mathematical Sciences Engels Russia Technol. and Qual. Mgmt. Saratov National Research State University Named after N. G. Chernyshevsky Department of Materials Science Saratov Russia Yuri Gagarin Saratov State Technical University Department of Information Security of Automated Systems Saratov Russia

ISBN: (数字)9781728143095

ISBN: (纸本)9781728143088

In this work, we solve the problem of predicting the dielectric constant of silicon-based nanocomposites using machine learning methods. Mathematical models and programs have been developed to predict the electrophysical properties of new materials, as well as to select the optimal composition. The results obtained will be useful for faster and cheaper creation of new functional composites. © 2020 IEEE.

关键词： Nanocomposites

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