Foundations of Computational Intelligence Volume 5

1. Front Matter

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2. Function Approximation and Classification: Theoretical Foundations

   1. Front Matter

      Pages 1-1

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   2. Feature Selection for Partial Least Square Based Dimension Reduction

      • Guo-Zheng Li, Xue-Qiang Zeng

      Pages 3-37

   3. Classification by the Use of Decomposition of Correlation Integral

      • Marcel Jiřina, Marcel Jiřina Jr.

      Pages 39-55

   4. Investigating Neighborhood Graphs for Inducing Density Based Clusters

      • Viviani Akemi Kasahara, Maria do Carmo Nicoletti

      Pages 57-78

   5. Some Issues on Extensions of Information and Dynamic Information Systems

      • Krzysztof Pancerz

      Pages 79-106

   6. A Probabilistic Approach to the Evaluation and Combination of Preferences

      • Annibal Parracho Sant’Anna

      Pages 107-125

   7. Use of the q-Gaussian Function in Radial Basis Function Networks

      • Renato Tinós, Luiz Otávio Murta Júnior

      Pages 127-145

3. Function Approximation and Classification: Success Stories and Real World Applications

   1. Front Matter

      Pages 147-147

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   2. Novel Biomarkers for Prostate Cancer Revealed by (α,β)-k-Feature Sets

      • Martín Gómez Ravetti, Regina Berretta, Pablo Moscato

      Pages 149-175

   3. A Tutorial on Multi-label Classification Techniques

      • André C. P. L. F. de Carvalho, Alex A. Freitas

      Pages 177-195

   4. Computational Intelligence in Biomedical Image Processing

      • Felix Bollenbeck, Udo Seiffert

      Pages 197-222

   5. A Comparative Study of Three Graph Edit Distance Algorithms

      • Xinbo Gao, Bing Xiao, Dacheng Tao, Xuelong Li

      Pages 223-242

   6. Classification of Complex Molecules

      • Francisco Torrens, Gloria Castellano

      Pages 243-315

   7. Intelligent Finite Element Method and Application to Simulation of Behavior of Soils under Cyclic Loading

      • A. A. Javadi, T. P. Tan, A. S. I. Elkassas

      Pages 317-338

   8. An Empirical Evaluation of the Effectiveness of Different Types of Predictor Attributes in Protein Function Prediction

      • Fernando Otero, Marc Segond, Alex A. Freitas, Colin G. Johnson, Denis Robilliard, Cyril Fonlupt

      Pages 339-357

   9. Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method

      • Marcel Jirina, Marcel Jirina Jr.

      Pages 359-376

4. Back Matter

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Foundations of Computational Intelligence Volume 5: Function Approximation and Classification Approximation theory is that area of analysis which is concerned with the ability to approximate functions by simpler and more easily calculated functions. It is an area which, like many other fields of analysis, has its primary roots in the mat- matics. The need for function approximation and classification arises in many branches of applied mathematics, computer science and data mining in particular. This edited volume comprises of 14 chapters, including several overview Ch- ters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of function approximation and classification. Besides research ar- cles and expository papers on theory and algorithms of function approximation and classification, papers on numerical experiments and real world applications were also encouraged. The Volume is divided into 2 parts: Part-I: Function Approximation and Classification – Theoretical Foundations Part-II: Function Approximation and Classification – Success Stories and Real World Applications Part I on Function Approximation and Classification – Theoretical Foundations contains six chapters that describe several approaches Feature Selection, the use Decomposition of Correlation Integral, Some Issues on Extensions of Information and Dynamic Information System and a Probabilistic Approach to the Evaluation and Combination of Preferences Chapter 1 “Feature Selection for Partial Least Square Based Dimension Red- tion” by Li and Zeng investigate a systematic feature reduction framework by combing dimension reduction with feature selection. To evaluate the proposed framework authors used four typical data sets.

• Extension
• Function Approximation
• algorithm
• algorithms
• approximation theory
• classification
• computational intelligence
• data mining
• information system
• intelligence
• mathematics

• Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, USA

  Ajith Abraham

• Faculty of Computers and Information, Information Technology Department, Cairo University, Orman

  Aboul-Ella Hassanien

• Dept. Computer Science, Technical University Ostrava, Ostrava, Czech Republic

  Václav Snášel

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