SS08:Machine Learning and Data Mining(ISICA 2021 Special Session)

Machine learning is to discover patterns and knowledge from existing data, and predict future events. By nature, many machine learning problems can be modelled as optimization problems, often with more than one conflicting objectives such as accuracy and complexity. It is also common that these problems have many locally optimal solutions. Traditional local optimization methods may not work well. For these reasons, Data Mining algorithms(DMA)have been widely used as an optimization tool in the field of machine learning in recent years.Learning methods based on Computational Intelligence techniques are widely used in different fields and the mentioned scalability requirements are a necessity to use them with huge databases.

On the other hand, ideas and techniques from machine learning can be used in and hybridized with DMA. It should also be a very promising research direction to study optimization problems from the machine learning point of view.

Scope

Potential authors for this Special Session should present original research and innovative results including (but not limited to) the following topics:

• Classification, One-class classification
• Clustering
• Association Mining
• Feature Selection/Extraction/Construction
• Instance Selection / Generation
• Rule and Tree Induction
• Statistical Learning and Modelling
• Kernels and Support Vector Machines
• Semi-supervised learning, Ensemble learning, Imbalanced learning, Manifold Learning, Lazy Learning, etc
• Real World Applications

 

Organizers

Feng Wang
WuHan University, China, fengwang@whu.edu.cn