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Set-valued prediction in multi-class classification 31

Set-valued prediction in multi-class classification

31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on machine learning, BNAIC/BENELEARN 2019

作者： Mortier, Thomas Wydmuch, Marek Dembczýnski, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznán 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. © 2019 for this paper by its authors.

关键词： Forecasting

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Improving BCI performance by modified common spatial patterns with robustly averaged covariance matrices

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World Congress on Medical Physics and Biomedical Engineering: Neuroengineering, Neural Systems, Rehabilitation and Prosthetics

作者： Kawanabe, M. Vidaurre, C. Intelligent Data Analysis Group Fraunhofer FIRST Kekulestr. 7 12489 Berlin Germany Machine Learning Group TU-Berlin Franklinstr. 28/29 10587 Berlin Germany

ISBN: (纸本)9783642038884

EEG single-trial analysis requires methods that are robust against noise and disturbance. In this contribution, based on the framework of robust statistics, we propose a simple modification of Common Spatial Patterns by the robust calculation of covariance estimators against outlying trials caused, for example, by artifacts. We tested the proposed robust filters with EEG recordings from 80 subjects and obtained, not only a significant improvement in performance, but for some subjects, also better neuro-physiologically interpretable filters. © 2009 Springer-Verlag.

关键词： Covariance matrix

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Combining Computer Vision and Word Processing to Classify Film Genres 25

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

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25th International Conference on Digital Signal Processing and its Applications, DSPA 2023

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

ISBN: (纸本)9798350337235

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. © 2023 IEEE.

关键词： learning algorithms

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Alleviating the influence of weak data asymmetries on Granger-causal analyses

Alleviating the influence of weak data asymmetries on Grange...

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10th International Conference on Latent Variable analysis and Signal Separation, LVA/ICA 2012

作者： Haufe, Stefan Nikulin, Vadim V. Nolte, Guido Berlin Institute of Technology Machine Learning Germany Bernstein Focus Neurotechnology Berlin Germany Charité University Medicine Neurophysics Berlin Germany Fraunhofer Institute FIRST Intelligent Data Analysis Berlin Germany

ISBN: (纸本)9783642285509

We introduce the concepts of weak and strong asymmetries in multivariate time series in the context of causal modeling. Weak asymmetries are by definition differences in univariate properties of the data, which are not necessarily related to causal relationships between time series. Nevertheless, they might still mislead (in particular Granger-) causal analyses. We propose two general strategies to overcome the negative influence of weak asymmetries in causal modeling. One is to assess the confidence of causal predictions using the antisymmetry-symmetry ratio, while the other one is based on comparing the result of a causal analysis to that of an equivalent analysis of time-reversed data. We demonstrate that Granger Causality applied to the SiSEC challenge on causal analysis of simulated EEG data greatly benefits from our suggestions. © 2012 Springer-Verlag.

关键词： Time series

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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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2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

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

ISBN: (纸本)9798350348293

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. © 2023 IEEE.

关键词： Computer vision

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Stochastic Principles of Optimality in Games with Nature and Their Application in Investment Management 14

Stochastic Principles of Optimality in Games with Nature and...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Gorelik, Victor Zolotova, Tatiana Moscow Pedagogical State University Department of Simulation Systems and Operations Research FRC CSC RAS Moscow Russia Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9781665412308

The principle of optimality in decision-making for games with nature, based on assessments of efficiency and risk, is proposed. In contrast to the traditional approach to the definition of a mixed strategy in game theory, this work considers the possibility of a correlation dependence of random values of payoffs for the original alternatives. © 2021 IEEE.

关键词： Game theory

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Fuzzy Logical-Linguistic analysis of data Quality in Financial Information Systems 16

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

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16th International Conference Management of Large-Scale System Development, MLSD 2023

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

ISBN: (纸本)9798350337907

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. © 2023 IEEE.

关键词： Semantics

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance 16

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

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16th International Conference Management of Large-Scale System Development, MLSD 2023

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

ISBN: (纸本)9798350337907

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. © 2023 IEEE.

关键词： Global warming

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Support vector novelty detection in hidden space

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Journal of Computational Information Systems 2011年第15期7卷 5581-5590页

作者： Zhang, Li Wang, Bangjun Li, Fanzhang He, Shuping Research Center of Machine Learning and Data Analysis School of Computer Science and Technology Soochow University Suzhou 215006 China

In this paper, a family of support vector novelty detection (or SVND) in hidden space is presented. Firstly a hidden-space SVND (or HSVND) algorithm is proposed. The data in an input space is mapped into a hidden space by using some hidden function. In the hidden space, we can perform the SVND with linear kernel. The advantage of HSVND over SVND is that a hidden function can be an arbitrary real-valued function including Mercer kernels and their combination. In SVND algorithm, only Mercer kernels can be used. Unfortunately, the mapped data may be non-spherical distributed data. In this case, HSVND works badly. Secondly a whitening HSVND (WHSVND) is proposed. We perform whitening method on the mapped data and then the SVND with linear kernel. The whitened data is spherical approximately. We prove that WHSVND is identical to whitening SVND (WSVND) if a single and same Mercer kernel is taken as a mapping function. In fact, above algorithms cannot work well if the data locates a subspace of input sample space or is rank deficiency. In this case, a mapping function only maps data from one subspace to another subspace and cannot make data spanning whole sample space. Thus noised and whitening HSVND (NWHSVND) is present for kernel mapping function. Before the data is mapped, some small randomly noise is put on it. Based on the noised data, the data is mapped into the hidden space. Next we whiten the mapped data, and perform the SVND with linear kernel. Experiments are performed on artificial and real-world data. © 2011 Binary Information Press December, 2011.

关键词： Vectors

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

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

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning 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: (纸本)9798350375718

关键词： Marketplaces

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