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Autor
Zagorecki Adam (Cranfield University)
Tytuł
Feature Selection for Naive Bayesian Network Ensemble using Evolutionary Algorithms
Źródło
Annals of Computer Science and Information Systems, 2014, vol. 2, s. 381 - 385, rys., bibliogr. 4 poz.
Słowa kluczowe
Straż pożarna, Algorytmy genetyczne, Modele bayesowskie, Data Mining
Firefighters, Genetic algorithms, Bayesian models, Data Mining
Uwagi
summ.
Abstrakt
This document describes the winning method for the AAIA'14 Data Mining Competition: Key risk factors for Polish State Fire Service. The competition challenge was a feature selection problem for a set of three classifiers, each of them in a form of ensemble of naive Bayes classifiers. The method described in this paper uses a genetic algorithm approach to identify an optimal set of variables used by the classifiers. The optimal set of variables is found through a three-stage procedure that involves different settings for the genetic algorithm. The first step leads to reduction of attribute set under consideration from 11,582 to 200 attributes. The following two steps focus on finding an optimal solution by first exploring the solution space and then refining the best solution found in an earlier step.(original abstract)
Pełny tekst
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Bibliografia
Pokaż
  1. Dietterich T., Overfitting and undercomputing in machine learning, ACM Comput. Surv. 27, (3), 326-327, 1995
  2. Geisser S., "The predictive sample reuse method with applications", J. Amer. Statist. Assoc., 70:320-328, 1975
  3. Holland J. H., "Adaptation in natural and artificial systems", Ann Arbor: The University of Michigan Press, 1975
  4. Mitchell M., "An Introduction to Genetic Algorithms", MIT Press, 1998
Cytowane przez
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ISSN
2300-5963
Język
eng
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