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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Marina Zhuravlyova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

The procedure of building control charts with variable control limits based on nonparametric bootstrap percentile method is proposed. It makes possible to construct control chart of any statistic with unknown continuous distribution and involves a large number of calculations. The order of building one of the scalar statistics to be monitored for the group of mutually dependent quality characteristics is considered. Formulas for obtaining variable control limits based on percentile method are shown. The issue of identifying the structure of relationships between quality characteristics is raised. Proposed control chart, Hotelling T2 and Shewhart usual control charts and their modifications for variable control limits have been constructed using actual data of two characteristics of the rolled steel manufacturing process. It is concluded that the development of the proposed approach can be useful in statistical process control.

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Methodology for Training data Science Skills Based on Competitions on the Kaggle Platform

Methodology for Training Data Science Skills Based on Compet...

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Information Computing and Processing (ICP), Seminar on

作者： Zaur Kh. Kalazhokov Yan T. Makoveichuk Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia

The article describes the experience of teaching students of Big data analytics-related fields the basics of data science. The teaching methodology used at the Financial University under the Government of the Russian Federation is given, which consists in involvement of students of the group to competitions on Google Kaggle platform. In this methodology, the positive factors influencing the rapid acquisition of initial data science skills are: competitive effect; availability of prepared datasets and problem formulations from the real business sector in various directions; possibility of instant verification of the found solution; practical unlimited number of solutions. These factors contribute to the rapid understanding by students of the need to understand the essence of the problem for the best results, obligatory familiarization with the data, data cleaning and formatting, exploratory data analysis, forming a model with some basic level and its improvement, as well as interpretation.

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Dynamic Approach to the analysis of Financial Structure: Overcoming the Bank-Based vs Market-Based Dichotomy 16

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

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

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

ISBN: (纸本)9798350337907

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. © 2023 IEEE.

关键词： Commerce

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ON SECOND-ORDER SCORING RULES FOR EPISTEMIC UNCERTAINTY QUANTIFICATION

arXiv

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arXiv 2023年

作者： Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium

It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture so-called aleatoric uncertainty of predictions, various machine learning methods have recently been developed with the goal to let the learner also represent its epistemic uncertainty, i.e., the uncertainty caused by a lack of knowledge and data. An emerging branch of the literature proposes the use of a second-order learner that provides predictions in terms of distributions on probability distributions. However, recent work has revealed serious theoretical shortcomings for second-order predictors based on loss minimisation. In this paper, we generalise these findings and prove a more fundamental result: There seems to be no loss function that provides an incentive for a second-order learner to faithfully represent its epistemic uncertainty in the same manner as proper scoring rules do for standard (first-order) learners. As a main mathematical tool to prove this result, we introduce the generalised notion of second-order scoring *** Codes 68T37 (Primary) 68T30 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Investor Risk Profile Determination Model 16

Investor Risk Profile Determination Model

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

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

ISBN: (纸本)9798350337907

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. © 2023 IEEE.

关键词： Convolution

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Determining the Genre of Music from a Melody Using machine learning and Parallel data Processing

Determining the Genre of Music from a Melody Using Machine L...

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Information Computing and Processing (ICP), Seminar on

作者： Danila A. Solodennikov Krystina A. Makoveichuk Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia

This article describes the author’s methodology for determining the genre of music from a melody using machine learning and parallel data processing. For determination is used dataset formed by the author, on the basis of which the model is trained and the genre is predicted on the test data. Yandex technologies, parallel programming, and Python data processing models are used in the work. There are also examples of training and test samples, model operation, data cleaning and dataset analysis methods. The work built neural network to identify genres and output results. The results vary from 35% to 50% and it should be noted that the dataset is rather small, so with bigger dataset we can await much higher result.

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Diverse subgroup set discovery

Diverse subgroup set discovery

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作者： Van Leeuwen, Matthijs Knobbe, Arno Machine Learning Department of Computer Science Katholieke Universiteit Leuven Leuven Belgium Algorithmic Data Analysis Department of Information and Computer Sciences Universiteit Utrecht Utrecht Netherlands Leiden Institute of Advanced Computer Science Universiteit Leiden Leiden Netherlands

Large data is challenging for most existing discovery algorithms, for several reasons. First of all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible. Second, many variants of essentially the same pattern exist, due to (numeric) attributes of high cardinality, correlated attributes, and so on. This causes top-k mining algorithms to return highly redundant result sets, while ignoring many potentially interesting results. These problems are particularly apparent with subgroup discovery (SD) and its generalisation, exceptional model mining. To address this, we introduce subgroup set discovery: one should not consider individual subgroups, but sets of subgroups. We consider three degrees of redundancy, and propose corresponding heuristic selection strategies in order to eliminate redundancy. By incorporating these (generic) subgroup selection methods in a beam search, the aim is to improve the balance between exploration and exploitation. The proposed algorithm, dubbed DSSD for diverse subgroup set discovery, is experimentally evaluated and compared to existing approaches. For this, a variety of target types with corresponding datasets and quality measures is used. The subgroup sets that are discovered by the competing methods are evaluated primarily on the following three criteria: (1) diversity in the subgroup covers (exploration), (2) the maximum quality found (exploitation), and (3) runtime. The results show that DSSD outperforms each traditional SD method on all or a (non-empty) subset of these criteria, depending on the specific setting. The more complex the task, the larger the benefit of using our diverse heuristic search turns out to be. © The Author(s) 2012.

关键词： Redundancy

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PITFALLS OF EPISTEMIC UNCERTAINTY QUANTIFICATION THROUGH LOSS MINIMISATION

arXiv

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arXiv 2022年

作者： Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful *** Codes 68T37 (Primary) 68T30 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Pitfalls in EEG-based brain effective connectivity analysis

Pitfalls in EEG-based brain effective connectivity analysis

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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011

作者： Haufe, Stefan Nikulin, Vadim V. Nolte, Guido Müller, Klaus-Robert Berlin Institute of Technology Machine Learning Germany Bernstein Focus Neurotechnology Berlin Germany Neurophysics Charité University Medicine Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Intelligent Data Analysis Fraunhofer Institute FIRST Berlin Germany

We consider the problem of estimating brain effective connectivity from electroencephalographic (EEG) measurements, which is challenging due to instantaneous correlations in the sensor data caused by volume conduction in the head. We present selected results of a larger realistic simulation study in which we tested the ability of various measures of effective connectivity to recover the information flow between the underlying sources, as well as the ability of linear and nonlinear inverse source reconstruction approaches to improve the estimation. It turns out that factors related to volume conduction dramatically limit the neurophysiological interpretability of sensor-space connectivity maps and may even (depending on the connectivity measure used) lead to conflicting results. The success of connectivity estimation on inverse source estimates crucially depends on the correctness of the source demixing. This in turn depends on the capability of the method to model (multiple) interacting sources, which is in general not achievable by linear inverses. © 2012 Springer-Verlag.

关键词： Inverse problems

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learning invariances with stationary subspace analysis

Learning invariances with stationary subspace analysis

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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Meinecke, Frank C. Von Bünau, Paul Kawanabe, Motoaki Müller, Klaus-R. Machine Learning Group Dept. Computer Science TU Berlin Franklinstr. 28/29 10587 Berlin Germany Intelligent Data Analysis Group Fraunhofer FIRST.IDA Kekuléstr. 7 12489 Berlin Germany

ISBN: (纸本)9781424444427

Recently, a novel subspace decomposition method, termed 'Stationary Subspace analysis' (SSA), has been proposed by Bünau et al. [10]. SSA aims to find a linear projection to a lower dimensional subspace such that the distribution of the projected data does not change over successive epochs or sub-datasets. We show that by modifying the loss function and the optimization procedure we can obtain an algorithm that is both faster and more accurate. We discuss the problem of indeterminacies and provide a lower bound on the number of epochs that is needed. Finally, we show in an experiment with simulated image patches, that SSA can be used favourably in invariance learning. ©2009 IEEE.

关键词： Invariance

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