<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.</p></abstract>

The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.

Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225 Gdynia, Poland

International Journal of Applied Mathematics and Computer Science

The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should...

Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Online veröffentlicht: 28. März 2011

Seitenbereich: 57 - 68

DOI: https://doi.org/10.2478/v10006-011-0004-3

Schlüsselwörter
data reduction, machine learning, A-Team, optimization, multi-agent system

This content is open access.

Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Online veröffentlicht: 28. März 2011

Seitenbereich: 57 - 68

DOI: https://doi.org/10.2478/v10006-011-0004-3

Schlüsselwörterdata reduction, machine learning, A-Team, optimization, multi-agent system

This content is open access.

Schlüsselwörter
data reduction, machine learning, A-Team, optimization, multi-agent system