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

Ireneusz Czarnowski; Piotr Jędrzejowicz

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 1, page 57-68
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Ireneusz Czarnowski, and Piotr Jędrzejowicz. "Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem." International Journal of Applied Mathematics and Computer Science 21.1 (2011): 57-68. <http://eudml.org/doc/208037>.

@article{IreneuszCzarnowski2011,
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.},
author = {Ireneusz Czarnowski, Piotr Jędrzejowicz},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {data reduction; machine learning; A-Team; optimization; multi-agent system},
language = {eng},
number = {1},
pages = {57-68},
title = {Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem},
url = {http://eudml.org/doc/208037},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Ireneusz Czarnowski
AU - Piotr Jędrzejowicz
TI - Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 1
SP - 57
EP - 68
AB - 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.
LA - eng
KW - data reduction; machine learning; A-Team; optimization; multi-agent system
UR - http://eudml.org/doc/208037
ER -

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Citations in EuDML Documents

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  1. Bogdan Trawiński, Magdalena Smętek, Zbigniew Telec, Tadeusz Lasota, Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms
  2. Liming Yuan, Jiafeng Liu, Xianglong Tang, Multiple-instance learning with pairwise instance similarity
  3. Norbert Jankowski, Graph-based generation of a meta-learning search space
  4. Piotr Kulczycki, Szymon Łukasik, An algorithm for reducing the dimension and size of a sample for data exploration procedures

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