Track: Quality Aspects in Machine Learning, AI and Data Analytics
Machine learning, AI and data analytics have become a major force of research progress in data mining and innovation across enterprises of all sizes. A lot of new platforms with increasingly more features for managing datasets have been proposed in recent years. Given that the datasets are frequently big, data mining is also related to the management of cloud and modern HPC clusters.
Quality assurance in machine learning, AI and data analytics is an important research and engineering challenge in today's data intensive computing. It can be directly related to the quality of data - poor quality data is predominant in many such systems. However, given the whole ecosystem surrounding the application of such approaches, quality assurance can also extend to many other aspects, such as the quality of the software implementing the approaches, of the services providing the approaches, of the management of data-intensive computing systems running the approaches, of the relevant resource and data management tools, of the handling of ethical concerns surrounding the use of the approaches, etc.
Papers on this track can explore any topics related to quality in machine learning, AI and data analytics. These include, but not limited to:
- Quality in data science
- Quality in deep learning
- Quality in business intelligence
- Quality in evolutionary algorithms
- Quality in fuzzy systems
- Data quality in distributed and streaming analytics
- Algorithms for detecting concept drifts / changes in the underlying distribution of incoming data
- Algorithms and approaches for detecting outliers, duplicated data, and inconsistent data
- Efficiency versus accuracy trade-off
- Data governance
- Big data quality management
- Big data quality metrics
- Big data management across distributed databases and datacentres
- Big data persistence and preservation
- Big data quality in cloud systems
- Testing of machine learning and AI software systems
- Software defect prediction
- Algorithms and approaches for data healing or system fault healing
- Procedures for evaluating data models
- Handling of ethical aspects in data mining
Chair: Leandro Minku, University of Birmingham, UK
- Agnieszka Jakóbik, Cracow University of Technology, Poland
- Alejandro Maté, University of Alicante, Spain
- Ana Respício, University of Lisbon, Portugal
- Chun Wai Chiu, University of Birmingham, UK
- Eduardo Spinosa, Federal University of Paraná, Brazil
- Honghui Du, University of Leicester, UK
- João Gama, University of Porto, Portugal
- Jorge Casillas, University of Granada, Spain
- Jose Manuel Molina Lopez, Universidad Carlos III de Madrid, Spain
- Shuo Wang, University of Birmingham, UK
Leandro L. Minku is a Lecturer in Intelligent Systems at the School of Computer Science, University of Birmingham (UK). Prior to that, he was a Lecturer in Computer Science at the University of Leicester (UK). He received his PhD degree in Computer Science from the University of Birmingham (UK) in 2010.
Dr. Minku's main research interests are machine learning in non-stationary environments / data stream mining, online class imbalance learning, ensembles of learning machines and computational intelligence for software engineering. His work has been published in internationally renowned journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.
Among other roles, Dr. Minku is the general chair for the International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2019 and 2020), the co-chair for the Artifacts Evaluation Track at the International Conference on Software Engineering (ICSE 2020), an associate editor for the Journal of Systems and Software, an editorial board member for Neurocomputing and a conference correspondent for IEEE Software.