3rd Evolutionary Data Mining and Machine Learning Workshop
Shanghai, China - Nov. 30th 2023
Data mining and machine learning is an important research area and becomes 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 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.
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:
Each workshop will solicit papers (max 8 pages plus 2 extra pages) for peer review. Please follow the IEEE ICDM 2023 Paper Submission Web Site to prepare the manuscript.
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.
The workshop submission link is IEEE ICDM Workshop 2023 Paper Submission Web Site. Please choose Number 34: Evolutionary Data Mining and Machine Learning (EDMML) to submit your workshop paper. Thanks!
8:00-8:05 - Opening Remarks
8.05-9.05 - Invited Speaker: Dr. Ruwang Jiao : Evolutionary Multi-objective Feature Selection for Machine Learning
9:05-9:25 - Paper Presentations: Information extraction of Chinese medical electronic records via evolutionary neural architecture search - Tian Zhang, Nan Li, Yuee Zhou, Wei Cai, and Lianbo Ma
9:25-9.45 - Paper Presentations: Generating assessment tests using image-based item - Doru Anastasiu Popescu, Doru Constantin, and Nicolae Bold
9.45-10:05 - Paper Presentations: Evolutionary Multi-model Federated Learning on Malicious and Heterogeneous Data - Chikai Shang, Fangqing Gu, and Jiaqi Jiang
10:05-10:25 - Paper Presentations: ICA model estimation using a mixed learning rule based on genetic algorithms and neural networks - Doru Constantin
10:25-10:45 - Paper Presentations: Enhancing Interpretability in AI-Generated Image Detection with Genetic Programming - Mingqian Lin, Lin Shang, and Xiaoying Gao
10.45-10.50 - Closing Remarks
Authors of all accepted papers will present their work in the session.
Paper Presentations: The presentations will each have a duration of 20 minutes (plus 5 minutes for questions).
Online Access: https://vuw.zoom.us/j/96403041133.
Title : Evolutionary Multi-objective Feature Selection for Machine Learning
Abstract: We are now in the era of big data, where vast amounts of high-dimensional data become ubiquitous in a variety of domains, such as social media, healthcare, and cybersecurity. When machine learning algorithms are applied to such high-dimensional data, they suffer from the curse of dimensionality, where the data becomes very sparse. Furthermore, the high-dimensional data contain redundant and/or irrelevant features that blur useful information from relevant features. Feature selection addresses the above issues by selecting a small subset of relevant features which can improve the learning performance, reduce the dimensionality of data, reduce space storage, improve computational efficiency, and facilitate data visualization and understanding. Feature selection plays a critical role in data mining, computational intelligence, and machine learning. Compared with other dimensionality reduction techniques, such as feature construction and feature extraction, feature selection can preserve the original semantics of the data, making it an effective method with interpretability and facilitating human understanding of the results. Feature selection is inherently a multi-objective problem. The two main goals of feature selection are to maximize the learning performance and minimize the number of selected features. However, these two objectives are usually in conflict. For example, removing relevant and/or complementary features can deteriorate learning performance. There is no single best feature subset, but rather a set of non-dominated subsets showing trade-offs between the two objectives. Optimizing the two objectives can more accurately reflect the decision-making reality of feature selection problems in practical applications. In this talk, the essential components in multi-objective feature selection such as solution representation, evaluation function, population initialization, offspring generation, population update, and decision making will be discussed extensively, and the strength and weakness of each category of methods will be summarized. Following that, some advanced topics related to multi-objective feature selection will be briefly discussed. Finally, this talk will also identify and summarize the major issues and challenges when using evolutionary multi-objective optimization methods for multi-objective feature selection, and suggest some possible future
Dr. Ruwang Jiao is currently a postdoctoral research fellow in artificial intelligence with the Centre for Data Science and Artificial Intelligence (CDSAI) & School of Engineering and Computer Science, Victoria University of Wellington (VUW). His research focuses mainly on big data reduction, feature selection, multi-objective machine learning, constrained optimization, and Bayesian optimization. He has published over 25 papers in fully refereed journals and conferences such as IEEE Transactions on Evolutionary Computation, Evolutionary Computation (MIT Press), IEEE Transactions on Cybernetics, IEEE Transactions on Antennas and Propagation, and Information Sciences.
Ying Bi is currently a distinguished professor with the School of Electrical and Information Engineering at Zhengzhou University, China. Her research focuses mainly on data mining, machine learning, computer vision, evolutionary computation, classification, image analysis, feature learning, and transfer learning. She has published an authored book and over 60 papers in fully refereed journals and conferences in evolutionary computation and machine learning.
She is a member of IEEE, IEEE Computational Intelligence Society (CIS), and the Association for Computing Machinery (ACM). She is the vice-chair of the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing and a member of the IEEE Computational Intelligence Society Task Force on Evolutionary Computation for Feature Selection and Construction. She has been serving as an organizing committee member of IEEE CEC 2019 and Australasian AI 2018, the workshop chair of IEEE CEC 2024, student affair chair of GECCO 2023, GECCO 2024, a guest editor of the journals of Applied Soft Computing, Memetic Computing and Algorithms, the organizer of the workshop on EDMML in IEEE ICDM 2023-2021 the organizer of the special sessions in IEEE WCCI/CEC 2023-2022, IEEE SSCI 2022, IEEE SSCI 2021, and IDEAL 2021. Dr Bi has been serving as a program committee member of over ten international conferences including IJCAI, GECCO, IEEE CEC, IEEE SSCI, and Australian AI. She is a co-chair Poster session at IEEE CEC 2019. She is serving as a reviewer of over ten international journals including IEEE Transactions on Cybernetics and IEEE Transactions on Evolutionary Computation.Bing Xue is currently a Professor of Artificial Intelligence and Program Director of Science in the School of Engineering and Computer Science at VUW. She has over 200 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, data mining, machine learning, classification, symbolic regression, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective machine learning. Dr Xue is currently the Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She was the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee.
Prof Xue is the organizer of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022. Prof Xue has been a chair for many international conferences including the Chair of Women@GECCO 2018, a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020, a co-Chair of the first Neuroevolution Track for GECCO 2021 and 2022. She is the Lead Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016, 2017, 2018, 2019, 2020, 2021, and 2022, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Finance Chair of 2019 IEEE Congress on Evolutionary Computation, a Workshop Chair of 2021 IEEE International Conference on Data Mining (ICDM), a Conference Activities Chair of 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), a General Co-Chair of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ 2020), a Tutorial Co-Chair of 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), a Publication Co-Chair of Proceedings of the 25th European Conference on Genetic Programming (EuroGP 2022), a Conference Chair of 2024 IEEE Congress on Evolutionary Computation. She is an Editor of the IEEE Computational Intelligence Society Newsletter. She is an Associate Editor or Member of the Editorial Board for over ten international journals, including IEEE Transactions on Evolutionary Computation, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, and ACM Transactions on Evolutionary Learning and Optimization.Mengjie Zhang is a Fellow of the Royal Society of New Zealand, a Fellow of IEEE, a Fellow of Engineering New Zealand, an IEEE CIS Distinguished Lecturer, and currently a Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.
His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimization and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimization, and clustering and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 700 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), ACM Transactions on Evolutionary Learning and Optimization, Genetic Programming and Evolvable Machines (Springer), IEEE Transactions on Emergent Topics in Computational Intelligence, Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html). He was the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020 and GECCO 2021. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). Since 2014, he has been co-organizing and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL, and also delivered a keynote/plenary talk for IEEE CEC 2018, IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017. Prof. Zhang is the Chair of IEEE CIS PhD Dissertation Award Committee and IEEE CIS Publication Strategic Planning Committee. He was the Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee, and the IEEE CIS Evolutionary Computation Technical Committee; a Vice-Chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the IEEE CIS Task Force on Evolutionary Deep Learning and Applications; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
For additional info please contact us Ying.Bi@ecs.vuw.ac.nz.
