References
- P.M. Granitto, P.F. Verdes, and H.A. Ceccatto, 'Neural network ensembles: evaluation of aggregation algorithms,' Artificial Intelligence, Vol.163, No.2, pp.139-162, 2005 https://doi.org/10.1016/j.artint.2004.09.006
- Zhi-Hua Zhou, Jianxin Wu, and Wei Tang, 'Ensembling neural networks: many could be better than all,' Artificial Intelligence, Vol.137, pp.239-263, 2002 https://doi.org/10.1016/S0004-3702(02)00190-X
- Chanho Park and Sung-Bae Cho, 'EVolutionary ensemble classifier for lymphoma and colon cancer classification,' Proc. of IEEE International Conf. on EVolutionary Computation, Vol.4, pp.2378-2385, 2003 https://doi.org/10.1109/CEC.2003.1299385
- N. Ueda, 'Optimal linear combination of neural networks for improving classification performance,' IEEE Tr. Pattern Analysis and Machine Intelligence, Vol.22, No.2, pp.207-215, 2000 https://doi.org/10.1109/34.825759
- L. Breiman, 'Bagging predictors,' Machine Learning, Vol.24, No.2, pp.123-140, 1996 https://doi.org/10.1007/BF00058655
- Robert Bryll, Ricardo Gutierrez-Osuna, and Francis Quek, 'Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets,' Pattern Recognition, Vol.36, No.6, pp.1291-1302, 2003 https://doi.org/10.1016/S0031-3203(02)00121-8
- J. R. Quinlan, 'Bagging, boosting, and C4.5,' Proc. AAAI-96, pp.725-730, 1996
- Tin Kam Ho, 'Multiple classifier combination: lessons and next steps,' in Hybrid Methods in Pattern Recognition, (Ed. by H. Bubke & A. Kandel), World Scientific, 2002
- Tin Kam Ho, 'The random subspace method for constructing decision forests,' IEEE Tr. Pattern Analysis and Machine Intelligence, Vol.20, No.8, pp.832-844, 1998 https://doi.org/10.1109/34.709601
- G. Tremblay, R. Sabourin, and P. Maupin, 'Optimizing nearest neighbour in random subspaces using a multi-objective genetic algorithm,' In Proc. of 17th ICPR, Vol.1, pp.208-211, 2004 https://doi.org/10.1109/ICPR.2004.662
- J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, 'On combining classifiers,' IEEE Tr. Pattern Analysis and Machine Intelligence, Vol.20, No.3, pp.226-239, 1998 https://doi.org/10.1109/34.667881
- Antanas Verikas, Arunas Lipnickas, Kerstin Malmqvist, Marija Bacauskiene, and Adas Gelzinis, 'Soft combination of neural classifiers: a comparative study,' Pattern Recognition Letters, Vol.20, No.4, pp429-444, 1999 https://doi.org/10.1016/S0167-8655(99)00012-4
- S. Wesolkowski and K. Hassanein, 'A comparative study of combination schemes for an ensemble of digit recognition neural networks,' Proc. of IEEE International Conf. on Computational Cybernetics and Simulation. Vol.4, pp.3534-3539, 1997 https://doi.org/10.1109/ICSMC.1997.633202
- G. Fumera and F. Roli, 'A theoretical and experimental analysis of linear combiners for multiple classifier systems,' IEEE Tr. Pattern Analysis and Machine Intelligence, Vol.27, No.6, pp.942-956, 2005 https://doi.org/10.1109/TPAMI.2005.109
- Hee- Joong Kang and David Doermann, 'Selection of classifiers for the construction of multiple classifier systems,' Proc. of International Conf. on Document Analysis and Recognition, Vol.2, pp.1194-1198, 2005 https://doi.org/10.1109/ICDAR.2005.213
- Il-Seok Oh, jin-Seon Lee, and Byung-Ro Moon, 'Hybrid genetic algorithms for feature selection,' IEEE Tr. Pattern Analysis and Machine Intelligence, Vol.26, No.11, pp.1424-1437, 2004 https://doi.org/10.1109/TPAMI.2004.105
- P. Jog, J. Suh, and D. Gucht, 'The effect of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem,' Proc. of International Conference on Genetic Algorithms, pp.110-115, 1989
- T.N. Bui and B.R. Moon, 'Genetic algorithm and graph partitioning,' IEEE Tr. Computers, Vol.45, No.7, pp.841-855, 1996 https://doi.org/10.1109/12.508322
- X. Zheng, B.A. Julstrom, and W. Cheng, 'Design of vector quantization codebooks using a genetic algorithm,' Proc. of IEEE International Conf. on EVolutionary Computation, pp.525-529, 1997 https://doi.org/10.1109/ICEC.1997.592366
- http://www.ics.uci.edu/~mlearn/databases/
- Yifeng Zhang and Siddhartha Bhattacharyya, 'Genetic progranuning in classifying large-scale data: an ensemble method,' Information Sciences, Vol.163, pp.85-101, 2004 https://doi.org/10.1016/j.ins.2003.03.028