大家好！ 我们在国际数据挖掘顶级会议–2023 IEEE International Conference on Data Mining（上海召开）上组织了第三届关于进化数据挖掘和机器学习的研讨会–EDMML: 3rd IEEE ICDM Workshop on Evolutionary Data Mining and Machine Learning。接收所有将进化计算算法应用于解决数据挖掘和机器学习相关问题的文章，欢迎大家积极投稿、转发！

研讨会重要日期如下：

论文投稿截止日期: 2023年9月17日

论文接收公布日期：2023年9月23日

最终版提交日期：2023年10月15日

更多信息，请见链接：https://yingbi92.github.io/EDMML_2023/

投稿链接：https://wi-lab.com/cyberchair/2023/icdm23/scripts/ws_submit.php?subarea=S

2021年的研讨会链接：【链接】进化数据挖掘和机器学习研讨会–IEEE 数据挖掘国际会议

2022年的研讨会链接：主题报告分享：Pareto Set Learning

任何问题，欢迎联系毕莹博士，邮箱：yingbi888@163.com, 微信：wingle7460

研讨会具体征稿信息如下:

Call for Papers

3rd Workshop on Evolutionary Data Mining and Machine Learning (EDMML) 

2023 IEEE International Conference on Data Mining (IEEE ICDM 2023)

https://www.cloud-conf.net/icdm2023/index.html

November 30th-December 3rd 2023,

 Shanghai, China

Data mining and machine learning is an important research area and becoming increasingly popular in various fields, such as security, engineering, sciences, finance, marketing, healthcare, and marketing. Data mining and machine learning cover a wide range of problems and tasks such as dimensionality reduction, classification and regression that need effective techniques/algorithms to solve. 

Evolutionary computation is a sub-field of artificial intelligence that includes a family of nature-inspired population-based algorithms/techniques. Evolutionary computation techniques have promising global search/optimization ability to find high-quality solutions to problems without requiring rich domain knowledge. Existing evolutionary computation paradigms include genetic algorithms (GAs), genetic programming (GP), evolutionary programming (EP), evolution strategies (ES), Learning classifier systems (LCS), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), evolutionary multi-objective optimization (EMO) and memetic computing (MC).

Evolutionary computation techniques have been successfully applied to solve many learning and optimization problems in data mining and machine learning, including classification, regression, clustering, dimensionality reduction, feature analysis, and visualization. However, the potential of evolutionary computation has not been comprehensively explored. Many problems in data mining and machine learning have not been well solved and the use of evolutionary computation may bring new ideas and solutions. On the other hand, evolutionary computation techniques require the development of representations, operators, fitness measures, and search mechanisms to well solve data mining and machine learning problems. In recent years, the topic of evolutionary data mining and machine learning becomes increasingly important and has attracted much attention from researchers and practitioners over the world. It is clear that there is a growing interest in utilizing evolutionary computation to address challenging tasks in data mining and machine learning.

Aim and Scope:

The theme of this workshop is the use of evolutionary computation for data mining and machine learning, covering ALL different evolutionary computation paradigms and their applications to data mining and machine learning.

The aim of this workshop is to investigate both the new theories and methods in different evolutionary computation paradigms on data mining and machine learning. This workshop will bring together researchers and practitioners from around the world to discuss the latest advances in the field and will act as a major forum for the presentation of recent research.

Authors are invited to submit their original and unpublished work to this workshop. Topics related to all aspects of evolutionary computation for data mining and machine learning, such as theories, algorithms, systems and applications, are welcome. 

Topics of interest include but are not limited to:

• Foundations of data mining

• Foundations of machine learning

• Algorithms of machine learning

• Feature selection and dimensionality reduction

• Feature learning and feature engineering

• Clustering and unsupervised learning

• Anomaly and/or outlier detection

• Classification and regression

• Ensemble learning

• Automated machine learning

• Missing or unbalanced data

• Data visualisation

• Instance selection

• Big data mining

• Stream data mining

• Multimedia data mining

• Social media mining

• Association

• Natural language processing and text mining

• Graph, web and multimedia mining

• Security, privacy and social impact of data mining

• Evolutionary transfer learning and multitasking

• Data-driven evolutionary computation

• Model-based evolutionary computation

• Evolutionary deep learning 

• Evolutionary computer vision and image analysis

• Evolutionary multi-objective for data mining and machine learning

• Data mining applications

• Machine learning applications

Important Dates:

• Paper submission deadline: September 17th, 2023

• Paper Acceptance Notification Date: September 23rd, 2023

• Camera Ready Paper: October 15th, 2023

• Workshop date: November 30th, 2023

• Conference dates: November 30th to December 3rd 2023

Paper Submission:

Each workshop will solicit papers (max 8 pages plus 2 extra pages) for peer review. Please follow the IEEE ICDM 2023 Submission Web Site （https://www.cloud-conf.net/icdm2023/call-for-papers.html）. Workshop papers are treated the same as regular conference papers. Please specify that your paper is submitted to the 34: Workshop on Evolutionary Data Mining and Machine Learning (EDMML).

All submissions will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. 

Organizers:

Ying Bi

Zhengzhou University, China.

yingbi888@163.com

Wechat(微信）: wingle7460

Bing Xue
Victoria University of Wellington, New Zealand.
Bing.Xue@ecs.vuw.ac.nz

Mengjie Zhang 
Victoria University of Wellington, New Zealand.
Mengjie.Zhang@ecs.vuw.ac.nz 

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