Swarm Intelligence for Multi-objective Problems in Data Mining

✍ Scribed by Satchidananda Dehuri, Susmita Ghosh (auth.), Carlos Artemio Coello Coello, Satchidananda Dehuri, Susmita Ghosh (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2009
• Tongue: English
• Leaves: 295
• Series: Studies in Computational Intelligence 242
• Edition: 1
• Category: Library

✦ Synopsis

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 (KDD) (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.

✦ Table of Contents

Front Matter....Pages -
An Introduction to Swarm Intelligence for Multi-objective Problems....Pages 1-17
Multi-Criteria Ant Feature Selection Using Fuzzy Classifiers....Pages 19-36
Multiobjective Particle Swarm Optimization in Classification-Rule Learning....Pages 37-64
Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers....Pages 65-92
Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization....Pages 93-114
A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective....Pages 115-155
Rigorous Runtime Analysis of Swarm Intelligence Algorithms – An Overview....Pages 157-177
Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach....Pages 179-198
The Basic Principles of Metric Indexing....Pages 199-232
Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windows....Pages 233-257
Combining Correlated Data from Multiple Classifiers....Pages 259-281
Back Matter....Pages -

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)