80 citations · 163 across the 14 of their papers we have counts for
6 papers · 1 filter
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines
Nikolay O. Nikitin, Maiia Pinchuk, Valerii Pokrovskii +7
Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are tas…
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment
Nikolay O. Nikitin, Sergey Teryoshkin, Valerii Pokrovskii +2
Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be us…
Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering
Petr Andriushchenko, Irina Deeva, Anna Bubnova +4
The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks…
Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines
Nikolay O. Nikitin, Pavel Vychuzhanin, Mikhail Sarafanov +6
The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the desi…
Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea
Nikolay O. Nikitin, Ilia Revin, Alexander Hvatov +2
The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimizat…
Automated data-driven approach for gap filling in the time series using evolutionary learning
Mikhail Sarafanov, Nikolay O. Nikitin, Anna V. Kalyuzhnaya
In the paper, we propose an adaptive data-driven model-based approach for filling the gaps in time series. The approach is based on the automated evolutionary identification of the…