Automated Evolutionary Approach for the Design of Composite Machine
Learning Pipelines

Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines

26 June 2021

Nikolay O. Nikitin

Pavel Vychuzhanin

Iana S. Polonskaia

Irina V. Barabanova

Anna V. Kaluzhnaya

A. Boukhanovsky

Papers citing "Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines"

16 / 16 papers shown

Title
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science Wonduk Seo Juhyeon Lee Yi Bu 48 0 0 30 Mar 2025
Online Meta-learning for AutoML in Real-time (OnMAR) Mia Gerber Anna Sergeevna Bosman J. D. Villiers OffRL 41 0 0 27 Feb 2025
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey Yang Gu Hengyu You Jian Cao Muran Yu Haoran Fan Shiyou Qian LM&MA AI4CE 46 3 0 11 Nov 2024
Automated data processing and feature engineering for deep learning and big data applications: a survey A. Mumuni F. Mumuni TPM 40 48 0 18 Mar 2024
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 65 5 0 13 Mar 2024
Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTM S. Khajoui Saeid Dehyadegari S. A. Jalaee 11 0 0 06 Jan 2024
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines Nikolay O. Nikitin Maiia Pinchuk Valerii Pokrovskii Peter Shevchenko Andrey Getmanov Yaroslav Aksenkin I. Revin Andrey Stebenkov Ekaterina Poslavskaya Anna V. Kaluzhnaya 26 0 0 22 Dec 2023
A knowledge-driven AutoML architecture C. Cofaru Johan Loeckx 23 0 0 28 Nov 2023
Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work Jennifer Rogers Anamaria Crisan 18 7 0 05 Apr 2023
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision Wenxin Jiang Vishnu Banna Naveen Vivek Abhinav Goel Nicholas Synovic George K. Thiruvathukal James C. Davis VLM 40 18 0 13 Mar 2023
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment Nikolay O. Nikitin Sergey Teryoshkin Valerii Pokrovskii Sergey Pakulin D. Nasonov 26 1 0 12 Jan 2023
On the balance between the training time and interpretability of neural ODE for time series modelling Yakov Golovanev A. Hvatov AI4TS 19 1 0 07 Jun 2022
Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea Nikolay O. Nikitin I. Revin A. Hvatov Pavel Vychuzhanin Anna V. Kaluzhnaya 13 19 0 03 Mar 2021
Automated data-driven approach for gap filling in the time series using evolutionary learning M. Sarafanov Nikolay O. Nikitin Anna V. Kaluzhnaya AI4TS 11 3 0 01 Mar 2021
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows Doris Xin Eva Yiwei Wu D. Lee Niloufar Salehi Aditya G. Parameswaran 53 91 0 13 Jan 2021
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 97 607 0 13 Mar 2020