Swarm Intelligence for Multi-objective Problems in Data Mining

Coello

ISBN 10: 3642036244 / ISBN 13: 9783642036248

Edité par Springer, 2009

A propos de cet article

pp. 304.

N° de réf. du vendeur 261376217

Détails bibliographiques

• Titre: Swarm Intelligence for Multi-objective Problems in Data Mining
• Éditeur: Springer
• Date de parution: 2009
• Langue: anglais
• ISBN à 10 chiffres: 3642036244
• ISBN à 13 chiffres: 9783642036248
• Reliure: Couverture rigide
• État de l'article: New

A propos de ce titre

Synopsis

An Introduction to Swarm Intelligence for Multi-objective Problems.- Multi-Criteria Ant Feature Selection Using Fuzzy Classifiers.- Multiobjective Particle Swarm Optimization in Classification-Rule Learning.- Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers.- Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization.- A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective.- Rigorous Runtime Analysis of Swarm Intelligence Algorithms - An Overview.- Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach.- The Basic Principles of Metric Indexing.- Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windows.- Combining Correlated Data from Multiple Classifiers.

Présentation de l'éditeur

The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining. Such a collection intends to illustrate the potential of multi-objective swarm intelligence techniques in data mining, with the aim of motivating more researchers in evolutionary computation and machine learning to do research in this field. This volume consists of eleven chapters, including an introduction that provides the basic concepts of swarm intelligence techniques and a discussion of their use in data mining. Some of the research challenges that must be faced when using swarm intelligence techniques in data mining are also addressed. The rest of the chapters were contributed by leading researchers, and were organized according to the steps normally followed in Knowledge Discovery in Databases (K.D.D.) (i.e., data preprocessing, data mining, and post processing). We hope that this book becomes a valuable reference for those wishing to do research on the use of multi-objective swarm intelligence techniques in data mining and knowledge discovery in databases.