Towards Advanced Data Analysis by Combining Soft Computing and Statistics

Soft computing, as an engineering science, and statistics, as a classical 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. 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 analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance.Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.

From the reviews:“This excellent volume will serve as an introduction to an important merging of viewpoints. The papers are generally very good and the text is clear and readable. The mathematical rigor is high and nearly all papers include real-world examples or experimental data. … This book is a valuable resource for those employed in statistical and soft computing, and a useful work for promoting better connections between these two fields.” (Creed Jones, ACM Computing Reviews, December, 2012)