2nd Evolutionary Data Mining and Machine Learning Workshop
Orlando, FL, USA - Nov. 30th 2022
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 2022 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..
USA Eastern Standard time
9:00-9:05 - Opening Remarks
9.05-10.00 - Invited Speaker: Xi Lin - Pareto Set Learning and its Applications in Machine Learning
10:00-10:20 - Paper Presentations A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification -
10:20-10.40 - Paper Presentations A Paraphrase Identification Approach in Paragraph length texts - Arwa Al saqaabi, Craig Stewart, eleni Akrida, and Alexandra Cristea
10.40-11:00 - Paper Presentations Using Genetic Programming to Identify Probability Distribution behind Data: A Preliminary Trial - Yang Syu and Chien-Min Wang
11:00-11:20 - Paper Presentations Deep Neural Networks and Data Augmentation for Standoff Detection of Dangerous Chemicals with Optimization Techniques - Eric Yao
11.20-11.25 - Closing Remarks
Title : Pareto Set Learning and its Applications in Machine Learning
Abstract: Multi-objective optimization problems can be found in many machine learning applications, such as multi-task learning, multi-objective Bayesian optimization, and neural multi-objective combinatorial optimization. These problems have multiple objectives to optimize, and no single solution can optimize all the objectives simultaneously. Different multi-objective optimization algorithms have been proposed to find a finite set of Pareto solutions with different optimal trade-offs among the objectives. However, it is well-known that the whole Pareto set is on a continuous manifold and can contain infinite solutions. The structural properties of the Pareto set are not well exploited in existing methods, and the finite-set approximation may not contain the most preferred solution(s) for decision-makers. In this talk, we will discuss how to develop a novel learning-based method to approximate the whole Pareto set for a given multi-objective optimization problem, which generalizes the decomposition-based multi-objective optimization algorithm (MOEA/D) from finite populations to models. With our proposed Pareto set model, decision-makers can easily explore any trade-off area in the approximate Pareto set for flexible decision-making. We will also present our current work on Pareto set learning for multi-objective Bayesian optimization and neural multi-objective combinatorial optimization.
Xi Lin is currently a Post Doctoral Research Fellow with the Department of Computer Science at the City University of Hong Kong. He received the B.Sc. Degree in 2013 from South China University of Technology, Guangzhou, China, the M.A. degree in 2015 from Columbia University, New York, United States, and the Ph.D. degree in 2020 from City University of Hong Kong, Hong Kong, China. His research interests include multi-objective optimization, multi-task learning, Bayesian optimization, evolutionary computation, and learning for optimization. His work has been published in top-tier machine learning conferences such as Conference on Neural Information Processing Systems (NeurIPS) and International Conference on Learning Representations (ICLR). He is a regular reviewer for top-tier machine learning and evolutionary computation conferences/journals, such as NeurIPS, ICML, ICLR, CEC, GECCO, JMLR, TMLR, and TEVC, and has received multiple top reviewer awards from ICML and ICLR.
Ying Bi is currently a postdoctoral research fellow in artificial intelligence with the School of Engineering and Computer Science at Victoria University of Wellington (VUW). 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 40 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 Association for Computing Machinery (ACM). She is the vice-chair of IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing and a member of 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, a guest editor of the journals of Memetic Computing and Algorithms, the organizer of the workshop on EDMML in IEEE ICDM 2021, the organizer of the special sessions in IEEE WCCI/CEC 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 co-chair Poster session in 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.
