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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.
Table des matières
Function Approximation and Classification: Theoretical Foundations.- Feature Selection for Partial Least Square Based Dimension Reduction.- Classification by the Use of Decomposition of Correlation Integral.- Investigating Neighborhood Graphs for Inducing Density Based Clusters.- Some Issues on Extensions of Information and Dynamic Information Systems.- A Probabilistic Approach to the Evaluation and Combination of Preferences.- Use of the q-Gaussian Function in Radial Basis Function Networks.- Function Approximation and Classification: Success Stories and Real World Applications.- Novel Biomarkers for Prostate Cancer Revealed by (?,?)-k-Feature Sets.- A Tutorial on Multi-label Classification Techniques.- Computational Intelligence in Biomedical Image Processing.- A Comparative Study of Three Graph Edit Distance Algorithms.- Classification of Complex Molecules.- Intelligent Finite Element Method and Application to Simulation of Behavior of Soils under Cyclic Loading.- An Empirical Evaluation of the Effectiveness of Different Types of Predictor Attributes in Protein Function Prediction.- Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method.
A propos de l'auteur
Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Résumé
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.
