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2011 | 21 | 1 | 57-68
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
21
Numer
1
Strony
57-68
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-04-13
poprawiono
2010-11-15
Twórcy
  • Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225 Gdynia, Poland
  • Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225 Gdynia, Poland
Bibliografia
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  • Kuncheva, L. and Bezdek, J. (1998). Nearest prototype classification: Clustering, genetic algorithm or random search?, IEEE Transactions on Systems, Man and Cybernetics 28(1): 160-164.
  • Kuncheva, L. and Jain, L. (1999). Nearest-neighbor classifier: Simultaneous editing and feature selection, Pattern Recognition Letters 20(11-13): 1149-1156.
  • Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin.
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  • Skalak, D. (1994). Prototype and feature selection by sampling and random mutation hill climbing algorithm, Proceedings of the International Conference on Machine Learning, New Brunswick, NJ, USA, pp. 293-301.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i1p57bwm
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