Titlebook: Swarm Intelligence for Multi-objective Problems in Data Mining; Carlos Artemio Coello Coello,Satchidananda Dehuri, Book 2009 Springer-Verl

intention

Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers,ision functions/decision rules in such a way that various performance aspects of classifiers (e.g., score of recognition and reliability) are . optimized..This chapter explains the applications of multi-objective swarm intelligence techniques (especially particle swarm optimization) on designing nov

BILL

Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization,e to a number of factors – core among these is their ease of comprehension, robust performance and fast data processing capabilities. Additionally feature selection is implicit within the decision tree structure..This chapter introduces the basic ideas behind decision trees, focusing on decision tre

织物

荨麻

,Rigorous Runtime Analysis of Swarm Intelligence Algorithms – An Overview,ost of these studies deal with evolutionary algorithms rather than swarm intelligence approaches such as ant colony optimization and particle swarm optimization. Despite the overwhelming practical success of swarm intelligence, the first runtime analyses of such approaches date only from 2006. Since

AGGER

Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach, of the most used representation form. However, the first issue in data mining is the computational complexity of the rule discovery process due to the huge amount of data. In this sense, this chapter proposes a novel approach based on a previous work that explores Multi-Objective Particle Swarm Opt

手势

The Basic Principles of Metric Indexing,ches to creating lower bounds using the metric axioms are discussed, such as pivoting and compact partitioning with metric ball regions and generalized hyperplanes. Finally, pointers are given for further exploration of the subject, including non-metric, approximate, and parallel methods.

Jacket

Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windt that restricts every customer to be served within a given time window. An approach for the VRPTW with the next three objectives is presented: 1)total distance (or time), 2)total waiting time, 3)number of vehicles. A data mining strategy, namely space partitioning, is adopted in this work. Optimal

河潭

Combining Correlated Data from Multiple Classifiers,om the sensor. The data is collected at different ranges and may have different statistical distributions and characteristics. Measurements from different classifiers are fused together to obtain more information about the phenomenon or environment under observation. Since the classifier fusion is a

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A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classtwo aforementioned objectives: classification accuracy and architectural complexity. The effectiveness of this method is shown on real life datasets having non-linear class boundaries. Empirical results indicate that the performance of the proposed method is encouraging.