Applying Ant Colony Optimization to configuring stacking ensembles for data mining

Aha, 1991, Instance-based learning algorithms, Machine Learning, 6, 37, 10.1007/BF00153759

Al-Ani, 2006, Feature subset selection using ant colony optimization, International Journal of Computational Intelligence, 2, 53

Bhattacharyya, 1999, Direct marketing performance modeling using genetic algorithms, INFORMS Journal on Computing, 11, 248, 10.1287/ijoc.11.3.248

Breiman, 1996, Bagging predictors, Machine Learning, 24, 123, 10.1007/BF00058655

Breiman, 2001, Random forest, Machine Learning, 45, 5, 10.1023/A:1010933404324

Campos, 2002, Ant colony optimization for learning bayesian networks, International Journal of Approximate Reasoning, 31, 291, 10.1016/S0888-613X(02)00091-9

Chan, 2006

Cleary, 1995, K∗: An instance-based learner using an entropic distance measure, 108

Cui, 2008, Model selection for direct marketing: Performance criteria and validation methods, Marketing Intelligence and Planning, 26, 275, 10.1108/02634500810871339

Demiröz, 1997, Classification by voting feature intervals, Vol. 1224, 85

Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7, 1

Dietterich, 2000

Dorigo, 2004

Duda, 2012

Džeroski, 2002, Vol. 2364, 201

Fan, W., Stolfo, S. J., Zhang, J., & Chan, P. K. (1999). AdaCost: Misclassification cost-sensitive boosting. In Proceedings of the sixteenth international conference on machine learning (pp. 97–105).

Fern, X. Z., & Brodley, C. E. (2003). Random projection for high dimensional data clustering: A cluster ensemble approach. In Machine learning-international workshop then conference (pp. 186–193).

Frank, A., & Asuncion, A. (2010). UCI machine learning repository. <http://archive.ics.uci.edu/ml>.

Frank, 1998, Generating accurate rule sets without global optimization, 144

Freund, Y. & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th international conference in machine learning (pp. 148–156).

Freund, 1997, Decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Science, 55, 119, 10.1006/jcss.1997.1504

Friedman, 1937, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association, 32, 675, 10.1080/01621459.1937.10503522

García, 2008, An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons, Journal of Machine Learning Research, 9, 2677

Goss, 1989, Self-organized shortcuts in the argentine ant, Naturwissenschaften, 76, 579, 10.1007/BF00462870

Gutjahr, 2002, Aco algorithms with guaranteed convergence to the optimal solution, Information Processing Letters, 82, 145, 10.1016/S0020-0190(01)00258-7

Hall, 2009, The WEKA data mining software: An update, SIGKDD Explorations, 11, 10, 10.1145/1656274.1656278

Holm, 1979, A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics, 6, 65

Holte, 1993, Very simple classification rules perform well on most commonly used datasets, Machine Learning, 11, 63, 10.1023/A:1022631118932

Iba, 1992, Induction of one-level decision trees, 233

John, 1995, Estimating continuous distributions in bayesian classifiers, 338

LeCessie, 1992, Ridge estimators in logistic regression, Applied Statistics, 41, 191, 10.2307/2347628

Ledezma, 2002, Heuristic and optimization for knowledge discovery

Liu, 2003, Classification rule discovery with ant colony optimization, 83

Lu, 2010, Ensemble pruning via individual contribution ordering, 871

Merz, 1999, Using correspondence analysis to combine classifiers, Machine Learning, 36, 33, 10.1023/A:1007559205422

Ordóñez, F. J., Ledezma, A., & Sanchis, A. (2008). Genetic approach for optimizing ensembles of classifiers. In Proceedings of the twenty-first international FLAIRS conference (pp. 89–94).

Parpinelli, 2002, Data mining with an ant colony optimization algorithm, IEEE Transactions on Evolutionary Computation, 6, 321, 10.1109/TEVC.2002.802452

Pinto, 2009, Using a local discovery ant algorithm for Bayesian network structure learning, IEEE Transactions on Evolutionary Computation, 13, 767, 10.1109/TEVC.2009.2024142

Polikar, 2006, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, 6, 21, 10.1109/MCAS.2006.1688199

Quinlan, 1993

Ramanathan, 2003

Schapire, 1990, The strength of weak learnability, Machine Learning, 5, 197, 10.1007/BF00116037

Seewald, A. K. (2002). How to make stacking better and faster while also taking care of an unknown weakness. In Proceedings of the 19th international conference on machine learning, ICML ’02 (pp. 554–561).

Sivagaminathan, 2007, A hybrid approach for feature subset selection using neural networks and ant colony optimization, Expert Systems with Applications, 33, 49, 10.1016/j.eswa.2006.04.010

Sun, 2007, Cost-sensitive boosting for classification of imbalanced data, Pattern Recognition, 40, 3358, 10.1016/j.patcog.2007.04.009

Ting, 1999, Issues in stacked generalization, Journal of Artificial Intelligence Research, 10, 271, 10.1613/jair.594

Todorovski, 2000, Combining multiple models with meta decision trees, 54

Wang, 2005, Classification rule mining with an improved ant colony algorithm, 357

Wolpert, 1992, Stacked generalization, Neural Networks, 5, 241, 10.1016/S0893-6080(05)80023-1

Wong, M. L. & Cui, G. (2010). Data mining using parallel multi-objective evolutionary algorithms on graphics hardware. In Proceedings of 2010 IEEE Congress on Evolutionary Computation (pp. 1–8).

Zahavi, 1997, Applying neural computing to target marketing, Journal of Interactive Marketing, 11, 5

Zhang, 2010, An ACO-based algorithm for parameter optimization of support vector machines, Expert Systems with Applications, 37, 6618, 10.1016/j.eswa.2010.03.067

Zheng, 2007, Constructing ensembles from data envelopment analysis, INFORMS Journal on Computing, 19, 486, 10.1287/ijoc.1060.0180

Zhou, 2001, Combining regression estimators: Ga-based selective neural network ensemble, International Journal of Computational Intelligence and Applications, 1, 341, 10.1142/S1469026801000287

Zhu, 2010, A hybrid approach for efficient ensembles, Decision Support Systems, 48, 480, 10.1016/j.dss.2009.06.007