Classification and Learning Using Genetic Algorithms

Transcription

1 Natural Computing Series Classification and Learning Using Genetic Algorithms Applications in Bioinformatics and Web Intelligence Bearbeitet von Sanghamitra Bandyopadhyay, Sankar Kumar Pal 1. Auflage Buch. xvi, 311 S. Hardcover ISBN Format (B x L): 15,5 x 23,5 cm Gewicht: 660 g Weitere Fachgebiete > EDV, Informatik > Datenbanken, Informationssicherheit, Geschäftssoftware > Datenkompression, Dokumentaustauschformate Zu Leseprobe schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, ebooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte.

2 Contents 1 Introduction Introduction Machine Recognition of Patterns: Preliminaries Data Acquisition Feature Selection Classification Clustering Different Approaches Connectionist Approach: Relevance and Features Genetic Approach: Relevance and Features Fuzzy Set-Theoretic Approach: Relevance and Features Other Approaches Applications of Pattern Recognition and Learning Summary and Scope of the Book Genetic Algorithms Introduction Traditional Versus Nontraditional Search Overview of Genetic Algorithms Basic Principles and Features Encoding Strategy and Population Evaluation Genetic Operators Parameters of Genetic Algorithms Schema Theorem Proof of Convergence of GAs Markov Chain Modelling of GAs Limiting Behavior of Elitist Model of GAs Some Implementation Issues in GAs Multiobjective Genetic Algorithms Applications of Genetic Algorithms

3 XII Contents 2.8 Summary Supervised Classification Using Genetic Algorithms Introduction Genetic Algorithms for Generating Fuzzy If Then Rules Genetic Algorithms and Decision Trees GA-classifier: Genetic Algorithm for Generation of Class Boundaries Principle of Hyperplane Fitting Region Identification and Fitness Computation Genetic Operations Experimental Results Results Consideration of Higher-Order Surfaces Summary Theoretical Analysis of the GA-classifier Introduction Relationship with Bayes Error Probability Relationship Between H opt and H GA Obtaining H GA from H How H GA Is Related to H opt Some Points Related to n and H Experimental Results Data Sets Learning the Class Boundaries and Performance on Test Data Variation of Recognition Scores with P Summary Variable String Lengths in GA-classifier Introduction Genetic Algorithm with Variable String Length and the Classification Criteria Description of VGA-Classifier Chromosome Representation and Population Initialization Fitness Computation Genetic Operators Theoretical Study of VGA-classifier Issues of Minimum miss and H Error Rate Experimental Results Data Sets Results

4 Contents XIII 5.6 VGA-classifier for the Design of a Multilayer Perceptron Analogy Between Multilayer Perceptron and VGA-classifier Deriving the MLP Architecture and the Connection Weights Postprocessing Step Experimental Results Summary Chromosome Differentiation in VGA-classifier Introduction GACD: Incorporating Chromosome Differentiation in GA Motivation Description of GACD Schema Theorem for GACD Terminology Analysis of GACD VGACD-classifier: Incorporation of Chromosome Differentiation in VGA-classifier Population Initialization Fitness Computation and Genetic Operators Pixel Classification of Remotely Sensed Image Relevance of GA Experimental Results Summary Multiobjective VGA-classifier and Quantitative Indices Introduction Multiobjective Optimization Relevance of Multiobjective Optimization Multiobjective GA-Based Classifier Chromosome Representation Fitness Computation Selection Crossover Mutation Incorporating Elitism PAES-classifier: The Classifier Based on Pareto Archived Evolution Strategy Validation and Testing Indices for Comparing MO Solutions Measures Based on Position of Nondominated Front Measures Based on Diversity of the Solutions Experimental Results Parameter Values

5 XIV Contents Comparison of Classification Performance Summary Genetic Algorithms in Clustering Introduction Basic Concepts and Preliminary Definitions Clustering Algorithms K-Means Clustering Algorithm Single-Linkage Clustering Algorithm Fuzzy c-means Clustering Algorithm Clustering Using GAs: Fixed Number of Crisp Clusters Encoding Strategy Population Initialization Fitness Computation Genetic Operators Experimental Results Clustering Using GAs: Variable Number of Crisp Clusters Encoding Strategy and Population Initialization Fitness Computation Genetic Operators Some Cluster Validity Indices Experimental Results Clustering Using GAs: Variable Number of Fuzzy Clusters Fitness Computation Experimental Results Summary Genetic Learning in Bioinformatics Introduction Bioinformatics: Concepts and Features Basic Concepts of Cell Biology Different Bioinformatics Tasks Relevance of Genetic Algorithms in Bioinformatics Bioinformatics Tasks and Application of GAs Alignment and Comparison of DNA, RNA and Protein Sequences Gene Mapping on Chromosomes Gene Finding and Promoter Identification from DNA Sequences Interpretation of Gene Expression and Microarray Data Gene Regulatory Network Identification Construction of Phylogenetic Trees for Studying Evolutionary Relationship DNA Structure Prediction RNA Structure Prediction

6 Contents XV Protein Structure Prediction and Classification Molecular Design and Docking Experimental Results Summary Genetic Algorithms and Web Intelligence Introduction Web Mining Web Mining Components and Methodologies Web Mining Categories Challenges and Limitations in Web Mining Genetic Algorithms in Web Mining Search and Retrieval Query Optimization and Reformulation Document Representation and Personalization Distributed Mining Summary A ɛ-optimal Stopping Time for GAs A.1 Introduction A.2 Foundation A.3 Fitness Function A.4 Upper Bound for Optimal Stopping Time A.5 Mutation Probability and ɛ-optimal Stopping Time B Data Sets Used for the Experiments C Variation of Error Probability with P References Index

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