Meu Porta-Voz: Sistema de Apoio à Participação Popular no Poder Legistativo.
Resumo
O atual cenário político do Brasil não fornece mecanismos eficazes para incluir a participação popular na tomada de decisão nas políticas públicas. Assim, o objetivo deste trabalho foi criar um aplicativo móvel a fim de oferecer ferramentas para a população discutir e sugerir soluções para os problemas encontrados nas cidades. Dessa forma, por meio do uso da Inteligência Coletiva, a plataforma é capaz de definir as prioridades do município. Por conseguinte, foi necessário escolher pessoas com um perfil específico para se afiliarem à plataforma e se candidatarem ao cargo político de vereador e serem os responsáveis por atender às demandas da população. Como resultado, um aplicativo móvel foi desenvolvido implementando todos os requisitos para atender os objetivos propostos. Um estudo de caso foi realizado em duas cidades do país: Ouro Preto - MG e Araraquara - SP, mostrando que o uso da tecnologia da informação pode auxiliar na participação popular na tomada de decisões políticas.
Palavras-chave:
Aplicativo político, participação popular na política, inteligência coletiva, eleições, política, opinião pública, administração colaborativa, e-participation
Referências
Aha, D., Kibler, D. and Albert, M. 1991. Instance-based learning algorithms. Machine Learning, vol.6, n.1, 37-66.
Alcala-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., Garcia, S., Sánchez, L. e Herrera, F. 2011. KEEL DataMining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. Journal of Multiple-Valued Logic and Soft Computing, vol.17, n.2-3, 255-287.
Antonelli, M., Ducange, P. and Marcelloni, F. 2012. Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach. IEEE Transactions on Fuzzy Systems, vol.20, n.2, 276-290.
Baluja, S. 1994. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Carnegie Mellon University, Pittsburgh - PA - USA.
Brighton, H. and Mellish, C. 2002. Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery, vol.6, n.2, 153-172.
Cano, J. R., Herrera, F. and Lozano, M. 2003. Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. IEEE Transactions on Evolutionary Computation, vol.7, n.6, 561- 575.
Chen, J.-H., Chen, H.-M. and Ho, S.-Y. 2005. Design of nearest neighbor classifiers: multi-objective approach. International Journal of Approximate Reasoning, vol.40, n.1–2, 3-22.
Coello, C. A. C. 2001. A Short Tutorial on Evolutionary Multiobjective Optimization. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, 21-40.
Coello, C. A. C., Lamont, G. B. and Veldhuizen, D. A. V. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems, 2. ed., Springer, New York.
Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. New York, John Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, vol.6, n.2, 182-197.
Eshelman, L. J. 1991. The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. Foundations of Genetic Algorithms. G. J. E. Rawlings, Morgan Kaufmann, 265-283.
Fazzolari, M., Giglio, B., Alcalá, R., Marcelloni, F. and Herrera, F. 2013. A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: Accuracy-complexity trade-off. Knowledge-Based Systems, vol.54, 32-41.
Fernández, A., López, V., del Jesus, M. J. and Herrera, F. 2015. Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems, vol.80, 109-121.
García-Pedrajas, N., Haro-García, A., and Pérez-Rodríguez, J. 2013. A scalable approach to simultaneous evolutionary instance and feature selection. Information Sciences, vol. 228, 150-174.
García-Pedrajas, N. and J. Pérez-Rodríguez 2012. "Multiselection of instances: A straightforward way to improve evolutionary instance selection." Applied Soft Computing, vol. 12, n.11, 3590-3602.
Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley.
Ho, S.-Y., Liu, C.-C., Liu, S. and Jou, J.-W. 2002. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognition Letters, vol.23, n.13, 1495–1503.
Liu, H. and H. Motoda 2002. On Issues of Instance Selection. Data Mining and Knowledge Discovery, vol. 6, n. 2, 115-130.
Miller, B. and D. Goldberg 1995. Genetic Algorithms, Tournament Selection, and the Effects of Noise. Complex Systems, vol. 9, 193–212.
Tsai, C.-F. and Chen, Z.-Y. 2014. Towards high dimensional instance selection: An evolutionary approach. Decision Support Systems, vol. 61, 79-92.
Tsai, C.-F., Chen, Z.-Y., and Ke, S.-W. 2014. Evolutionary instance selection for text classification. The Journal of Systems and Software, vol. 90, 104-113.
Whitley, D. 1989. The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In Proceedings of the Third International Conference on Genetic Algorithms. George Mason University, USA, Morgan Kaufmann Publishers Inc., 116-121.
Wilson, D. R. and T. Martinez 2000. Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning, vol. 38, n. 3, 257-286.
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J. and Steinberg, D. 2008. Top 10 algorithms in data mining. Knowledge and Information Systems, vol. 14, n. 1, 1-37.
Zitzler, E. and L. Thiele 1999. Multiobjective evolutionary algorithms: a comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation, vol. 3, n. 4, 257-271.
Zitzler, E., Laumanns, M. and Thiele, L. 2001. SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report, Swiss Federal Institute of Technology, Department of Electrical Engineering.
Alcala-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., Garcia, S., Sánchez, L. e Herrera, F. 2011. KEEL DataMining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. Journal of Multiple-Valued Logic and Soft Computing, vol.17, n.2-3, 255-287.
Antonelli, M., Ducange, P. and Marcelloni, F. 2012. Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach. IEEE Transactions on Fuzzy Systems, vol.20, n.2, 276-290.
Baluja, S. 1994. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Carnegie Mellon University, Pittsburgh - PA - USA.
Brighton, H. and Mellish, C. 2002. Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery, vol.6, n.2, 153-172.
Cano, J. R., Herrera, F. and Lozano, M. 2003. Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. IEEE Transactions on Evolutionary Computation, vol.7, n.6, 561- 575.
Chen, J.-H., Chen, H.-M. and Ho, S.-Y. 2005. Design of nearest neighbor classifiers: multi-objective approach. International Journal of Approximate Reasoning, vol.40, n.1–2, 3-22.
Coello, C. A. C. 2001. A Short Tutorial on Evolutionary Multiobjective Optimization. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, 21-40.
Coello, C. A. C., Lamont, G. B. and Veldhuizen, D. A. V. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems, 2. ed., Springer, New York.
Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. New York, John Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, vol.6, n.2, 182-197.
Eshelman, L. J. 1991. The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. Foundations of Genetic Algorithms. G. J. E. Rawlings, Morgan Kaufmann, 265-283.
Fazzolari, M., Giglio, B., Alcalá, R., Marcelloni, F. and Herrera, F. 2013. A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: Accuracy-complexity trade-off. Knowledge-Based Systems, vol.54, 32-41.
Fernández, A., López, V., del Jesus, M. J. and Herrera, F. 2015. Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems, vol.80, 109-121.
García-Pedrajas, N., Haro-García, A., and Pérez-Rodríguez, J. 2013. A scalable approach to simultaneous evolutionary instance and feature selection. Information Sciences, vol. 228, 150-174.
García-Pedrajas, N. and J. Pérez-Rodríguez 2012. "Multiselection of instances: A straightforward way to improve evolutionary instance selection." Applied Soft Computing, vol. 12, n.11, 3590-3602.
Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley.
Ho, S.-Y., Liu, C.-C., Liu, S. and Jou, J.-W. 2002. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognition Letters, vol.23, n.13, 1495–1503.
Liu, H. and H. Motoda 2002. On Issues of Instance Selection. Data Mining and Knowledge Discovery, vol. 6, n. 2, 115-130.
Miller, B. and D. Goldberg 1995. Genetic Algorithms, Tournament Selection, and the Effects of Noise. Complex Systems, vol. 9, 193–212.
Tsai, C.-F. and Chen, Z.-Y. 2014. Towards high dimensional instance selection: An evolutionary approach. Decision Support Systems, vol. 61, 79-92.
Tsai, C.-F., Chen, Z.-Y., and Ke, S.-W. 2014. Evolutionary instance selection for text classification. The Journal of Systems and Software, vol. 90, 104-113.
Whitley, D. 1989. The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In Proceedings of the Third International Conference on Genetic Algorithms. George Mason University, USA, Morgan Kaufmann Publishers Inc., 116-121.
Wilson, D. R. and T. Martinez 2000. Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning, vol. 38, n. 3, 257-286.
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J. and Steinberg, D. 2008. Top 10 algorithms in data mining. Knowledge and Information Systems, vol. 14, n. 1, 1-37.
Zitzler, E. and L. Thiele 1999. Multiobjective evolutionary algorithms: a comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation, vol. 3, n. 4, 257-271.
Zitzler, E., Laumanns, M. and Thiele, L. 2001. SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report, Swiss Federal Institute of Technology, Department of Electrical Engineering.
Publicado
17/05/2017
Como Citar
MACHADO, Tales; JR., Álvaro; PAIVA, Debora.
Meu Porta-Voz: Sistema de Apoio à Participação Popular no Poder Legistativo.. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 13. , 2017, Lavras.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 277-284.
DOI: https://doi.org/10.5753/sbsi.2017.6053.
