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Volumn 7, Issue 2-3, 2013, Pages 229-254

Learning Bayesian network classifiers using ant colony optimization

Author keywords

Ant colony optimization (ACO); Bayesian network classifiers; Classification; Data mining

Indexed keywords

ANT COLONY OPTIMIZATION; CLASSIFICATION (OF INFORMATION); DATA MINING; HEURISTIC ALGORITHMS; KNOWLEDGE REPRESENTATION; LARGE DATASET; MINERS; SUPPORT VECTOR MACHINES;

EID: 84880778798     PISSN: 19353812     EISSN: 19353820     Source Type: Journal    
DOI: 10.1007/s11721-013-0087-6     Document Type: Article
Times cited : (31)

References (45)
  • 8
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-348.
    • (1992) Machine Learning , vol.9 , pp. 309-348
    • Cooper, G.F.1    Herskovits, E.2
  • 10
    • 68349117241 scopus 로고    scopus 로고
    • Learning Bayesian network equivalence classes with ant colony optimization
    • Daly, R., & Shen, Q. (2009). Learning Bayesian network equivalence classes with ant colony optimization. Journal of Artificial Intelligence Research, 35, 391-447.
    • (2009) Journal of Artificial Intelligence Research , vol.35 , pp. 391-447
    • Daly, R.1    Shen, Q.2
  • 11
    • 79959427276 scopus 로고    scopus 로고
    • Using ant colony optimization in learning Bayesian network equivalence classes
    • Palo Alto: AAAI Press
    • Daly, R., Shen, Q., & Aitken, S. (2006). Using ant colony optimization in learning Bayesian network equivalence classes. In UK workshop on computational intelligence (UKCI) (pp. 111-118). Palo Alto: AAAI Press.
    • (2006) UK Workshop on Computational Intelligence (UKCI) , pp. 111-118
    • Daly, R.1    Shen, Q.2    Aitken, S.3
  • 12
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple datasets
    • Demšar, J. (2006). Statistical comparisons of classifiers over multiple datasets. Journal of Machine Learning Research, 7, 1-30.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 13
    • 0002012598 scopus 로고    scopus 로고
    • The ant colony optimization meta-heuristic
    • New York: McGraw-Hill
    • Dorigo, M., & Di Caro, G. (1999). The ant colony optimization meta-heuristic. In New ideas in optimization (Vol. 2, pp. 11-32). New York: McGraw-Hill.
    • (1999) New Ideas in Optimization , vol.2 , pp. 11-32
    • Dorigo, M.1    Di Caro, G.2
  • 16
    • 0033084695 scopus 로고    scopus 로고
    • Ant algorithms for discrete optimization
    • Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137-172.
    • (1999) Artificial Life , vol.5 , Issue.2 , pp. 137-172
    • Dorigo, M.1    Di Caro, G.2    Gambardella, L.M.3
  • 18
    • 0000220520 scopus 로고    scopus 로고
    • Learning Bayesian networks with local structure
    • Norwell: Kluwer
    • Friedman, N., & Goldszmidt, M. (1998). Learning Bayesian networks with local structure. In Learning in graphical models (pp. 421-460). Norwell: Kluwer.
    • (1998) Learning in Graphical Models , pp. 421-460
    • Friedman, N.1    Goldszmidt, M.2
  • 20
    • 58149287952 scopus 로고    scopus 로고
    • An extension on statistical comparisons of classifiers over multiple datasets for all pairwise comparisons
    • García, S., & Herrera, F. (2008). An extension on statistical comparisons of classifiers over multiple datasets for all pairwise comparisons. Journal of Machine Learning Research, 9, 2677-2694.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 2677-2694
    • García, S.1    Herrera, F.2
  • 21
    • 34249761849 scopus 로고
    • Learning Bayesian networks: the combination of knowledge and statistical data
    • Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20, 197-244.
    • (1995) Machine Learning , vol.20 , pp. 197-244
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 22
    • 79951515446 scopus 로고    scopus 로고
    • An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
    • Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51, 141-154.
    • (2011) Decision Support Systems , vol.51 , pp. 141-154
    • Huysmans, J.1    Dejaeger, K.2    Mues, C.3    Vanthienen, J.4    Baesens, B.5
  • 24
    • 79954601044 scopus 로고    scopus 로고
    • A hybrid method for learning Bayesian networks based on ant colony optimization
    • Ji, J., Hu, R., Zhang, H., & Liu, C. (2011). A hybrid method for learning Bayesian networks based on ant colony optimization. Applied Soft Computing, 11, 3373-3384.
    • (2011) Applied Soft Computing , vol.11 , pp. 3373-3384
    • Ji, J.1    Hu, R.2    Zhang, H.3    Liu, C.4
  • 26
    • 0002610991 scopus 로고    scopus 로고
    • Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches
    • San Francisco: Morgan Kaufmann
    • Keogh, E., & Pazzani, M. (1999). Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches. In International workshop on artificial intelligence and statistics (pp. 225-230). San Francisco: Morgan Kaufmann.
    • (1999) International Workshop on Artificial Intelligence and Statistics , pp. 225-230
    • Keogh, E.1    Pazzani, M.2
  • 31
    • 78650705743 scopus 로고    scopus 로고
    • Editorial survey: swarm intelligence for data mining
    • Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82, 1-42.
    • (2011) Machine Learning , vol.82 , pp. 1-42
    • Martens, D.1    Baesens, B.2    Fawcett, T.3
  • 34
    • 0035210113 scopus 로고    scopus 로고
    • Acceptance of rules generated by machine learning among medical experts
    • Pazzani, M. J., Mani, S., & Shankle, W. R. (2001). Acceptance of rules generated by machine learning among medical experts. Methods of Information in Medicine, 40, 380-385.
    • (2001) Methods of Information in Medicine , vol.40 , pp. 380-385
    • Pazzani, M.J.1    Mani, S.2    Shankle, W.R.3
  • 37
    • 78049251857 scopus 로고    scopus 로고
    • Extensions to the Ant-Miner classification rule discovery algorithm
    • LNCS, Heidelberg: Springer
    • Salama, K. M., & Abdelbar, A. M. (2010). Extensions to the Ant-Miner classification rule discovery algorithm. In LNCS: Vol. 6234. 7th international conference on swarm intelligence (ANTS 2010) (pp. 43-50). Heidelberg: Springer.
    • (2010) 7th International Conference on Swarm Intelligence (ANTS 2010) , vol.6234 , pp. 43-50
    • Salama, K.M.1    Abdelbar, A.M.2
  • 38
    • 79961130807 scopus 로고    scopus 로고
    • Exploring different rule quality evaluation functions in ACO-based classification algorithms
    • Piscataway: IEEE Press
    • Salama, K. M., & Abdelbar, A. M. (2011). Exploring different rule quality evaluation functions in ACO-based classification algorithms. In IEEE symposium on swarm intelligence (SIS) (pp. 1-8). Piscataway: IEEE Press.
    • (2011) IEEE Symposium on Swarm Intelligence (SIS) , pp. 1-8
    • Salama, K.M.1    Abdelbar, A.M.2
  • 40
    • 82355186008 scopus 로고    scopus 로고
    • Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm
    • Salama, K. M., Abdelbar, A. M., & Freitas, A. A. (2011). Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm. Swarm Intelligence, 5, 149-182.
    • (2011) Swarm Intelligence , vol.5 , pp. 149-182
    • Salama, K.M.1    Abdelbar, A.M.2    Freitas, A.A.3
  • 41
    • 84869412276 scopus 로고    scopus 로고
    • Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery
    • Salama, K. M., Abdelbar, A. M., Otero, F. E., & Freitas, A. A. (2013). Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery. Applied Soft Computing, 13, 667-675.
    • (2013) Applied Soft Computing , vol.13 , pp. 667-675
    • Salama, K.M.1    Abdelbar, A.M.2    Otero, F.E.3    Freitas, A.A.4
  • 42
    • 84880778829 scopus 로고    scopus 로고
    • UCI Repository of machine learning databases
    • UCI Repository of machine learning databases. Retrieved Oct. 2011 from. http://www. ics. uci. edu/~mlearn/MLRepository. html.
    • (2011) Retrieved
  • 45
    • 79959397993 scopus 로고    scopus 로고
    • Two novel ant colony optimization approaches for Bayesian network structure learning
    • Piscataway: IEEE Press
    • Yanghui, Wu., McCall, J., & Corne, D. (2010). Two novel ant colony optimization approaches for Bayesian network structure learning. In IEEE world congress on evolutionary computation (CEC 2010) (pp. 1-7). Piscataway: IEEE Press.
    • (2010) IEEE World Congress on Evolutionary Computation (CEC 2010) , pp. 1-7
    • Yanghui, W.1    McCall, J.2    Corne, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.