Towards Advanced Data Analysis by Combining Soft Computing and Statistics
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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.
Advances research in soft computing and statical methods for data analysis
Written by leading experts in the field
"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)
This book blends the adaptability and speed of soft computing with the rigor and precision of statistics to enhance the robustness and generalizability of data analysis, while preserving the flexibility to solve real-world problems intuitively and efficiently.