NebFuzz: Um Novo Algoritmo de Agrupamento Semi-Supervisionado Baseado no Fuzzy C-Means
Resumo
Agrupamento semi-supervisionado utiliza dados não rotulados, juntamente com dados rotulados, com a finalidade de melhorar o desempenho dos algoritmos. Este trabalho apresenta um novo algoritmo de agrupamento semi-supervisionado baseado no algoritmo Fuzzy C-Means. O novo algoritmo é avaliado em relação à dois algoritmos de agrupamento semi-supervisionados a partir de dados parcialmente rotulados nas tarefas de classificação e de agrupamento. Além disso, o comportamento do algoritmo é discutido e os resultados validados com taxa de acerto, índice de Rand corrigido e intervalos com 95% de confiança. Desse modo, foi possível certificar que o novo algoritmo de agrupamento semi-supervisionado apresenta desempenho melhor quando há poucos dados rotulados disponíveis.Referências
Amini, M. R. and Gallinari, P. (2005). Semi-supervised learning with an imperfect supervisor. Knowledge and Information Systems, 8:385–413.
Bezdek, J. (1981). Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum.
Bouchachia, A. (2007). Learning with parly data. Neural Computing and application, (16):267–293.
Bouchachia, A. and Pedrycz, W. (2006). Data clustering with partial supervision. Data Mining and Knowledge Discovery, (12):47–78.
Chapelle, O., Zien, A., and Scholkopf, B. (2006). Semi-supervised learning. MIT Press.
Costa, I. G., Carvalho, F. A. D., and de Souto, M. C. (2003). Comparative study on proximity indices for cluster analysis of gene expression time series. Journal of Intelligent & Fuzzy Systems, 13:133–142.
Hathaway, R. J., Bezdek, J., and Hu, Y. (2000). Generalized fuzzy c-means clustering strategies using lp-norm distances. IEEE Transaction on Fuzzy Systems, 8(5):576–582.
Jain, A. K. and Dubes, R. C. (1988). Algorithms for clustering data. Prentice Hall, New Jersey.
Mitchel, T. (1997). Machine Learning. McGraw Hill.
Nigam, K., McCallum, A. K., Thrun, S., and Mitchell, T. (2000). Text classification from labeled and unlabeled documents using em. Machine Learning, 39:103–134.
Pedrycz, W. and Waletzky, J. (1997). Fuzzy clustering with partial supervision. IEEE transactions on system, man and cybernetics, 27(5).
Stepp, R. E. and Michalski, R. S. (1986). Machine Learning: An Artificial Intelligence Approach, volume 2, chapter Conceptual Clustering: Inventing Goal-Oriented Classifictions of Structured Objects, pages 471–478. Morgan Kaufmann.
Zhu, X. (2008). Semi-Supervised Learning Literature Survey. Carnegie Mellon University.
Bezdek, J. (1981). Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum.
Bouchachia, A. (2007). Learning with parly data. Neural Computing and application, (16):267–293.
Bouchachia, A. and Pedrycz, W. (2006). Data clustering with partial supervision. Data Mining and Knowledge Discovery, (12):47–78.
Chapelle, O., Zien, A., and Scholkopf, B. (2006). Semi-supervised learning. MIT Press.
Costa, I. G., Carvalho, F. A. D., and de Souto, M. C. (2003). Comparative study on proximity indices for cluster analysis of gene expression time series. Journal of Intelligent & Fuzzy Systems, 13:133–142.
Hathaway, R. J., Bezdek, J., and Hu, Y. (2000). Generalized fuzzy c-means clustering strategies using lp-norm distances. IEEE Transaction on Fuzzy Systems, 8(5):576–582.
Jain, A. K. and Dubes, R. C. (1988). Algorithms for clustering data. Prentice Hall, New Jersey.
Mitchel, T. (1997). Machine Learning. McGraw Hill.
Nigam, K., McCallum, A. K., Thrun, S., and Mitchell, T. (2000). Text classification from labeled and unlabeled documents using em. Machine Learning, 39:103–134.
Pedrycz, W. and Waletzky, J. (1997). Fuzzy clustering with partial supervision. IEEE transactions on system, man and cybernetics, 27(5).
Stepp, R. E. and Michalski, R. S. (1986). Machine Learning: An Artificial Intelligence Approach, volume 2, chapter Conceptual Clustering: Inventing Goal-Oriented Classifictions of Structured Objects, pages 471–478. Morgan Kaufmann.
Zhu, X. (2008). Semi-Supervised Learning Literature Survey. Carnegie Mellon University.
Publicado
19/07/2011
Como Citar
MACÁRIO, Valmir; CARVALHO, Francisco de A. T. de.
NebFuzz: Um Novo Algoritmo de Agrupamento Semi-Supervisionado Baseado no Fuzzy C-Means. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 240-250.
ISSN 2763-9061.
