Towards Advanced Data Analysis by Combining Soft Computing and Statistics

✍ Scribed by Angela Blanco-Fernández, María Rosa Casals (auth.), Christian Borgelt, María Ángeles Gil, João M.C. Sousa, Michel Verleysen (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 377
• Series: Studies in Fuzziness and Soft Computing 285
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages 1-7
Arithmetic and Distance-Based Approach to the Statistical Analysis of Imprecisely Valued Data....Pages 1-18
Linear Regression Analysis for Interval-valued Data Based on Set Arithmetic: A Review....Pages 19-31
Bootstrap Confidence Intervals for the Parameters of a Linear Regression Model with Fuzzy Random Variables....Pages 33-42
On the Estimation of the Regression Model M for Interval Data....Pages 43-52
Hybrid Least-Squares Regression Modelling Using Confidence Bounds....Pages 53-63
Testing the Variability of Interval Data: An Application to Tidal Fluctuation....Pages 65-74
Comparing the Medians of a Random Interval Defined by Means of Two Different L 1 Metrics....Pages 75-86
Comparing the Representativeness of the 1-norm Median for Likert and Free-response Fuzzy Scales....Pages 87-98
Fuzzy Probability Distributions in Reliability Analysis, Fuzzy HPD-regions, and Fuzzy Predictive Distributions....Pages 99-106
SAFD — An R Package for Statistical Analysis of Fuzzy Data....Pages 107-118
Statistical Reasoning with Set-Valued Information: Ontic vs. Epistemic Views....Pages 119-136
Pricing of Catastrophe Bond in Fuzzy Framework....Pages 137-150
Convergence of Heuristic-based Estimators of the GARCH Model....Pages 151-163
Lasso–type and Heuristic Strategies in Model Selection and Forecasting....Pages 165-176
Streaming-Data Selection for Gaussian-Process Modelling....Pages 177-190
Change Detection Based on the Distribution of p-Values....Pages 191-203
Advanced Analysis of Dynamic Graphs in Social and Neural Networks....Pages 205-222
Fuzzy Hyperinference-Based Pattern Recognition....Pages 223-240
Dynamic Data-Driven Fuzzy Modeling of Software Reliability Growth....Pages 241-252
Dynamic Texture Recognition Based on Compression Artifacts....Pages 253-266
The Hubness Phenomenon: Fact or Artifact?....Pages 267-278
Proximity-Based Reference Resolution to Improve Text Retrieval....Pages 279-290
Derivation of Linguistic Summaries is Inherently Difficult: Can Association Rule Mining Help?....Pages 291-303
Mining Local Connectivity Patterns in fMRI Data....Pages 305-317
Fuzzy Clustering based on Coverings....Pages 319-330
Decision and Regression Trees in the Context of Attributes with Different Granularity Levels....Pages 331-342
Stochastic Convergence Analysis of Metaheuristic Optimisation Techniques....Pages 343-357
Comparison of Multi-objective Algorithms Applied to Feature Selection....Pages 359-375
Back Matter....Pages 0--1

✦ Subjects

Computational Intelligence; Probability and Statistics in Computer Science; Data Mining and Knowledge Discovery; Simulation and Modeling