References
- Chen, D., Yang, Y., & Dong, Z. (2016). An incremental algorithm for attribute reduction with variable precision rough sets. Applied Soft Computing, 45, 129–149.10.1016/j.asoc.2016.04.003
- da Silva, A., de Oliveira, W., & Ludermir, T. (2016). Weightless neural network parameters and architecture selection in a quantum computer. Neurocomputing, 183, 13–22.10.1016/j.neucom.2015.05.139
- D’eer, L., Cornelis, C., & Yao, Y. (2016). A semantically sound approach to Pawlak rough sets and covering-based rough sets. International Journal of Approximate Reasoning, 78, 62–72.10.1016/j.ijar.2016.06.013
- Gandhia, V., Prasad, G., Coyle, D., Behera, L., & McGinnity, T. M. (2015). Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques. Neurocomputing, 170, 161–167.10.1016/j.neucom.2014.12.114
- Gao, H., Gao, F., & Wang, D. (2015). Quantum artificial neural networks with applications. Information science, 290, 1–6.
- Gunji, Y. P., Sonoda, K., & Basios, V. (2016). Quantum cognition based on an ambiguous representation derived from a rough set approximation. Biosystems, 141, 55–66.10.1016/j.biosystems.2015.12.003
- Hassan, Y. F. (2011). Rough sets for adapting wavelet neural networks as a new classifier system. Springer Applied Intelligence Journal, 35, 260–268.10.1007/s10489-010-0218-3
- Huanga, S., & Chen, M. (2016). Constructing optimized interval type-2 TSK neuro-fuzzy systems with noise reduction property by quantum inspired BFA. Neurocomputing, 173, Part 3, 1839–1850.10.1016/j.neucom.2015.09.060
- Janicki, R., & Lenarčič, R. (2016). Optimal approximations with Rough Sets and similarities in measure spaces. International Journal of Approximate Reasoning, 71, 1–14.10.1016/j.ijar.2015.12.014
- Liu, H.-L. (2016). Acoustic partial discharge localization methodology in power transformers employing the quantum genetic algorithm. Applied Acoustics, 102, 71–78.10.1016/j.apacoust.2015.08.011
- Melnichenko, G. (2010). Energy discriminant analysis, quantum logic, and fuzzy sets. Journal of Multivariate Analysis, 101, 68–76.10.1016/j.jmva.2009.04.011
- Moro, S., Laureano, R., & Cortez, P. (2011, October). Using data mining for bank direct marketing: An application of the CRISP-DM methodology. In P. Novais, et al. (Eds.), Proceedings of the European Simulation and Modelling Conference – ESM’2011 (pp. 117–121). Guimarães.
- Pan, R., Zhang, Z., Fan, Y., Cao, J., Lu, K., & Yang, T. (2016). Multi-objective optimization method for learning thresholds in a decision-theoretic rough set model. International Journal of Approximate Reasoning, 71, 34–49.10.1016/j.ijar.2016.01.002