Call For Papers
WCCI2024 & IJCNN2024 Special Session on:
"MO-AutoML: Multi-Objective Automated Machine Learning"
The IEEE World Congress on Computational Intelligence, June 30 - July 5, 2024, YOKOHAMA, JAPAN
The IEEE World Congress on Computational Intelligence, June 30 - July 5, 2024, YOKOHAMA, JAPAN
Background and Aim
With the aim of automatically locating machine learning configurations with the best performance possible in constrained resources and without human involvement, automated machine learning (AutoML) views the machine learning task as a configuration search problem (i.e., optimization problem). This approach reduces the machine's reliance on human experts by enabling automatic feature processing, algorithm model selection and modeling, parameter tuning, and other tasks. AutoML has been demonstrated to either match or surpass the results of human experts manually fine-tuning parameters in numerous domains, and it can significantly lower the expenses associated with implementing and utilizing machine learning. It has become one of the most popular and cutting-edge research directions in the field of artificial intelligence and machine learning.
Early AutoML are primarily driven by a single objective. However, machine learning applications in real-world naturally have more than one objective. For example, different prediction measures may be conflict objectives. In addition to prediction measures, some other potential objectives are model complexity, time consumption, and power consumption. When designing AutoML, considering multiple objectives is herein much important.
This special session aims to provide a forum for researchers and practitioners to present advanced studies in AutoML, especially the MO-AutoML.
Scope
The topics include, but are not limited to:
Advanced techniques and applications on AutoML,
Multiobjective automated feature engineering, such as feature selection, and feature extraction,
Multiobjective automated Neural Architecture Search (NAS),
Multiobjective automated hyperparameter optimization,
Multiobjective automated data augmentation,
Multiobjective automated data cleaning,
Multiobjective model compression,
Multiobjective model combination,
Multiobjective ensemble learning,
MO-AutoML framework,
MO-AutoML for different learning tasks, e.g., classification/regression, unsupervised, semi-supervised, self-supervised, few-shot, transfer learning,
Real-world applications with AutoML and MO-AutoML.
Submissions
Papers should be submitted by following the instructions at the IEEE WCCI 2024 website. Please select the International Joint Conference on Neural Networks (IJCNN) and the Special Session on “MO-AutoML: Multiobjective Automated Machine Learning”. Accepted papers will be included and published in the conference proceedings by IEEE Explore, which are typically indexed by EI.
Important Dates
15th January 2024: Paper Submission Deadline
29th January 2024: Paper Submission Deadline
15th March 2024: Paper Acceptance Notification
1st May 2024: Final Paper Submission & Early Registration Deadline
30th June 2024 - 5th July 2024: Conference at Yokohama, Japan
Organizers
Zhongyi Hu, Associate Professor, School of Information Management, Wuhan University, China (Zhongyi.hu@whu.edu.cn)
Mustafa Misir, Associate Professor, Division of Natural and Applied Sciences, Duke Kunshan University, China (mustafa.misir@dukekunshan.edu.cn)
Yi Mei, Associate Professor, School of Engineering and Computer Science, Victoria University of Wellington, New Zealand (yi.mei@ecs.vuw.ac.nz)
About WCCI2024 and IJCNN2024
World Congress on Computational Intelligence (WCCI 2024) is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The International Joint Conference on Neural Networks (IJCNN), the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and the IEEE Congress on Evolutionary Computation (IEEE CEC).
The International Joint Conference on Neural Networks (IJCNN) covers a wide range of topics in the field of neural networks, from biological neural networks to artificial neural computation.