The main objective of the COST action IC0702 "Combining Soft Computing Techniques and Statistical Methods to Improve Data Analysis Solutions" is to strengthen the dialogue between the statistics and soft computing research communities in order to cross-pollinate both fields and generate mutual improvement activities.
Soft computing, as an engineering science, and statistics, as a branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications (context of discovery, model generation). In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty.
Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analysing the possible situations and their (relative) likelihood (context of justification, model validation). It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance.
Bringing the two fields closer together will enhance the robustness and generalisability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.
The lectures and tutorials in this summer course point out the potential that lies in joint solutions and the transfer of ideas from one field to the other. They are intended to stimulate young researchers to explore methods from that field - soft computing or statistics - they are not so familiar with yet in order to broaden their view and trigger new ideas for fruitful interdisciplinary research.