An agent-based simulated annealing algorithm for data reduction

Authors:
Ireneusz Czarnowski;Piotr Jędrzejowicz
Affiliations:
Department of Information Systems, Gdynia Maritime University, Gdynia, Poland;Department of Information Systems, Gdynia Maritime University, Gdynia, Poland
Venue:
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Year:
2010

Citing 9
Cited 1

Simulated annealing: A pedestrian review of the theory and some applications

Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
C4.5: programs for machine learning

C4.5: programs for machine learning
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Data mining: concepts and techniques

Data mining: concepts and techniques
Instance Selection and Construction for Data Mining

Instance Selection and Construction for Data Mining
Selecting representative examples and attributes by a genetic algorithm

Intelligent Data Analysis
The COMPSET algorithm for subset selection

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining

Applied Soft Computing

JABAT middleware as a tool for solving optimization problems

Transactions on computational collective intelligence II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data reduction in the supervised machine learning aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction can result in increased capabilities and generalization properties of the learning model and shorter learning process time. It can also help in scaling up to a large data sources. This paper proposes an approach based on a combination of the simulated annealing technique and the multi-agent architecture designed for solving the data reduction problem. The paper includes the overview of the proposed approach and shows the computational experiment results. Experiment has shown that the proposed agent-based simulated annealing outperforms the traditional simulated annealing approach when solving the data reduction problem.