80 citations · 183 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
MIxBN: library for learning Bayesian networks from mixed data
Anna V. Bubnova, Irina Deeva, Anna V. Kalyuzhnaya
This paper describes a new library for learning Bayesian networks from data containing discrete and continuous variables (mixed data). In addition to the classical learning methods…
Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks
Irina Deeva, Anna Bubnova, Petr Andriushchenko +4
In this paper, a multipurpose Bayesian-based method for data analysis, causal inference and prediction in the sphere of oil and gas reservoir development is considered. This allows…
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…
Multi-Objective Evolutionary Design of Composite Data-Driven Models
Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin +2
In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogene…
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…