Aboul Ella Hassanien - Machine Learning Paradigms: Theory and Application

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligencequickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.

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This edited volume comprises 22 chapters, including several overview chapters, which provide an up-to-date and state-of-the art research covering the theory and algorithms of machine learning and bio-inspiring optimization. Besides research articles and expository papers on theory and algorithms of machine learning and bio-inspiring optimization, papers on numerical experiments and real-world applications were also encouraged. The book is divided into three main parts.

Machine Learning in Feature Selection part contains three chapters that discuss different feature selection methods using different machine learning technologies such as Genetic Algorithm, Weighting Attributes and Decision Rules, and Greedy Selection of Attributes.

The part on Machine Learning in Classification and Ontology contains 10 chapters. It discusses the application of machine learning techniques in classification in different areas like Remote Sensing, Fault Diagnosis, Intelligent Ontology-Based Solution for Energy Management, Personalized Learning Experience Using Reinforcement Learning, and Online Sentiment Analysis.

Bio-inspiring Optimization and Applications is the third part of the book. It contains nine chapters discussing the application of bio-inspiring optimization techniques in Task Scheduling, Parameters Optimization, Global Optimization, and Global Sequence Alignment Problem.

We are very much grateful to the authors of this volume and the reviewers for their great effort by reviewing and providing useful feedback to the authors. The editors would like to express thanks to Dr. Thomas Ditzinger (Springer Engineering Inhouse Editor, Studies in Computational Intelligence Series), Prof. Janusz Kacprzyk (Editor-in-Chief, Springer Studies in Computational Intelligence Series), and all Editorial Assistant, Springer Verlag, Heidelberg for the editorial assistance and excellent collaboration to produce this important scientific work. We hope that the reader will share our joy and will find the volume useful.