Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging (Wiley Series in Bioinformatics) - Hardcover

Über die Autorin bzw. den Autor

PRADIPTA MAJI, PhD, is Assistant Professor in the Machine Intelligence Unit of the Indian Statistical Institute. His research explores pattern recognition, bioinformatics, medical image processing, cellular automata, and soft computing.

SANKAR K. PAL, PhD, is Director and Distinguished Scientist of the Indian Statistical Institute. He is also a J. C. Bose Fellow of the Government of India. Dr. Pal founded both the Machine Intelligence Unit and the Center for Soft Computing Research at the Indian Statistical Institute. He is a Fellow of the IEEE, IAPR, IFSA, TWAS, and Indian National Science Academy.

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Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing

Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection.

Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as:

• Soft computing in pattern recognition and data mining
• A mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set
• Selection of non-redundant and relevant features of real-valued data sets
• Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis
• Segmentation of brain MR images for visualization of human tissues

Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text--covering the latest findings as well as directions for future research--is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.