Meta-learning for resampling recommendation systems

Smolyakov, Dmitry; Korotin, Alexander; Erofeev, Pavel; Papanov, Artem; Burnaev, Evgeny

doi:10.1117/12.2523103

Cited by 18 publications

(14 citation statements)

References 25 publications

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“…Analysis of eSports data requires the development of specialized machine learning methods: there are outliers due to faulty sensors, part of the data is represented in the form of multidimensional time series, and another part is provided as heterogeneous events streams, etc. Thus we should develop a unified methodology for eSports data processing, including pre-processing data to remove anomalies [2,17,28], methods of imbalanced classification [9,31] and dimensionality reduction [20,21] to extract specific patterns and predict rare events.…”

Section: Discussionmentioning

confidence: 99%

Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis

Korotin

Khromov

Stepanov

et al. 2019

2019 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

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eSports is a developing multidisciplinary research area. At present, there is a lack of relevant data collected from real eSports athletes and lack of platforms which could be used for the data collection and further analysis. In this paper, we present a sensing system for enabling the data collection from professional athletes. Also, we report on the case study about collecting and analyzing the gaze data from Monolith professional eSports team specializing in Counter-Strike: Global Offensive (CS:GO) discipline. We perform a comparative study on assessing the gaze of amateur players and professional athletes. The results of our work are vital for ensuring eSports data collection and the following analysis in the scope of scouting or assessing the eSports players and athletes.

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Section: Discussionmentioning

confidence: 99%

Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis

Korotin

Khromov

Stepanov

et al. 2019

2019 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

Self Cite

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“…The problem at hand is imbalanced: the number of earthquakes with big enough magnitudes is small. So it is natural to apply machine learning heuristics that can deal with a natural class imbalance, see [10,27]. Here we consider two simple yet efficient techniques for resampling: we modify our initial training sample to make more emphasis on minor class objects -Oversampling increase weights of the minor class objects in a random way, -Undersampling drop some major class objects to balance number of instances of each class in the training sample.…”

Section: Resampling Techniquesmentioning

confidence: 99%

“…There are a number of resampling techniques that can deal with imbalanced classification problems [11,10,27], see subsection 4.4 above. Here we consider classification with oversampling and undersampling, as well as no-resampling case.…”

Section: Usage Of Resampling Techniquesmentioning

confidence: 99%

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Usage of Multiple RTL Features for Earthquakes Prediction

Proskura

Zaytsev

Braslavsky

et al. 2019

Computational Science and Its Applications – ICCSA 2019

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We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given moment of time. A common approach is to use expert forecasts based on features like Region-Time-Length (RTL) characteristics. The proposed approach uses machine learning on top of multiple RTL features to take into account effects at various scales and to improve prediction accuracy. For historical data about Japan earthquakes 1992-2005 and predictions at locations given in this database the best model has precision up to ∼ 0.95 and recall up to ∼ 0.98.

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“…These numbers indicate that the dataset is imbalanced and, as a result, in case of multiclass classification data points of slug and annular mist flow regimes could influence significantly the training process leading to the miss-classification errors. In order to balance data and improve classification accuracy approaches to imbalanced classification can be used [47,48].…”

mentioning

confidence: 99%

A predictive model for steady-state multiphase pipe flow: Machine learning on lab data

Kanin

Осипцов

Vainshtein

et al. 2019

Journal of Petroleum Science and Engineering

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Engineering simulators used for steady-state multiphase flows in oil and gas wells and pipelines are commonly utilized to predict pressure drop and phase velocities. Such simulators are typically based on either empirical correlations (e.g., Beggs and Brill, Mukherjee and Brill, Duns and Ros) or first-principles mechanistic models (e.g., Ansari, Xiao, TUFFP Unified, Leda Flow Point model, OLGAS). The simulators allow one to evaluate the pressure drop in a multiphase pipe flow with acceptable accuracy. However, the only shortcoming of these correlations and mechanistic models is their applicability (besides steady-state versions of transient simulators such as Leda Flow and OLGA). Empirical correlations are commonly applicable in their respective ranges of data fitting; and mechanistic models are limited by the applicability of the empirically based closure relations that are a part of such models. In order to extend the applicability and the accuracy of the existing accessible methods, a method of pressure drop calculation in the pipeline is proposed. The method is based on well segmentation and calculation of the pressure gradient in each segment using three surrogate models based on Machine Learning (ML) algorithms trained on a representative lab data set from the open literature. The first model predicts the value of a liquid holdup in the segment, the second one determines the flow pattern, and the third one is used to estimate the pressure gradient. To build these models, several ML algorithms are trained such as Random Forest, Gradient Boosting Decision Trees, Support Vector Machine, and Artificial Neural Network, and their predictive abilities are cross-compared. The proposed method for pressure gradient calculation yields R 2 = 0.95 by using the Gradient Boosting algorithm as compared with R 2 = 0.92 in case of Mukherjee and Brill correlation and R 2 = 0.91 when a combination of Ansari and Xiao mechanistic models is utilized. The application of the above-mentioned ML algorithms and the larger database used for their training will allow extending the proposed methodology to a wider applicability range of input parameters as compared to standard accessible techniques. The method for pressure drop prediction based on ML algorithms trained on lab data is also validated on three real field cases. Validation indicates that the proposed model yields the following coefficients of determination: R 2 = 0.806, 0.815 and 0.99 as compared with the highest values obtained by commonly used techniques: R 2 = 0.82 (Beggs and Brill correlation), R 2 = 0.823 (Mukherjee and Brill correlation) and R 2 = 0.98 (Beggs and Brill correlation). Hence, the method for calculating the pressure distribution could give comparable or even higher scores on field data by contrast to correlations and mechanistic models. This fact is an indicator that the model can be scalable from the lab to the field conditions without any additional retraining of ML algorithms.

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Meta-learning for resampling recommendation systems

Cited by 18 publications

References 25 publications

Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis

Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis

Usage of Multiple RTL Features for Earthquakes Prediction

A predictive model for steady-state multiphase pipe flow: Machine learning on lab data

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