Meu Porta-Voz: Support System for Popular Participation in Legislature Power.
Abstract
The current political brazilian scenario does not provide effective mechanisms to include public participation in the decision-making of government policies. Thus, the objective of this work was to create a mobile app that offers tools for the population to discuss and suggest solutions to problems found in cities. In this way, through the use of Collective Intelligence, the platform is able to define the priorities of the city. It was necessary to choose people with specific profile to join the platform and to apply for the Political office of councilor. These people are responsible for solving the demands of the population. As result, a mobile application was developed implementing all the requirements to achieve the objectives. A case study was performed in two cities of the country: Ouro Preto - MG and Araraqua - SP. We concluded that the use of information technology can assist in the public participation of political decision-making.
Keywords:
Aplicativo político, participação popular na política, inteligência coletiva, eleições, política, opinião pública, administração colaborativa, e-participation
References
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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.
Published
2017-05-17
How to Cite
MACHADO, Tales; JR., Álvaro; PAIVA, Debora.
Meu Porta-Voz: Support System for Popular Participation in Legislature Power.. In: BRAZILIAN SYMPOSIUM ON INFORMATION SYSTEMS (SBSI), 13. , 2017, Lavras.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 277-284.
DOI: https://doi.org/10.5753/sbsi.2017.6053.
