Selección de atributos relevantes aplicando algoritmos que combinan conjuntos aproximados y optimización en colonias de hormigas

• Yanela Rodríguez ^[1] ; Yumilka Fernández ^[1] ; Rafael Bello ^[2] ; Yailé Caballero ^[1]
  1. [1] Universidad de Camagüey

     Universidad de Camagüey

     Cuba

     
  2. [2] Universidad Central de Las Villas

     Universidad Central de Las Villas

     Cuba

     
• Localización: Revista Cubana de Ciencias Informáticas, ISSN-e 2227-1899, Vol. 8, Nº. 1, 2014
• Idioma: español
• Títulos paralelos:
  • Feature selection applying algorithms base on rough set and ant colony optimization
• Enlaces
  • Texto completo
• Dialnet Métricas: 1 Cita
• Resumen
  • español

    La selección de atributos relevantes puede ser vista como uno de los problemas más importantes en el campo del aprendizaje automático. En esta investigación se hace un análisis sobre los métodos de selección de atributos; haciendo énfasis en aquellos que emplean técnicas de Optimización en Colonias de Hormigas (ACO) y la Teoría de los Conjuntos Aproximados (RST). Se propone además, un sistema que permite la generación automatizada de los subconjuntos de rasgos principales que describen los datos, a través de cualquiera de los algoritmos tratados en esta investigación. Por otro lado se implementaron e incluyeron en el sistema algoritmos como el clásico QUICKREDUCT y otros encontrados en la bibliografía. Para verificar la eficiencia de los métodos estudiados se llevaron a cabo experimentos con bases de casos internacionales y se realizaron comparaciones con otros métodos. Además estos métodos se aplicaron en el preprocesamiento de los datos para pronosticar, de forma automatizada, las temperaturas diarias en el Centro Meteorológico de Camagüey. Los resultados demostraron que los algoritmos implementados proveen una solución eficiente al problema de selección de rasgos.

  • English

    Feature selection can be viewed as one of the most fundamental problems in the field of machine learning. An analysis on the methods of feature selection is done in this investigation; stressing those that use techniques of Ant Colony Optimization and the Rough Set Theory. Also, in this investigation it is proposed a system that allows the generation automatized of the subsets of principal features that describe the data, through any of algorithms studied in this investigation. Moreover, algorithms were implemented and included in the system, like the classical QUICKREDUCT and some others found in the bibliography. To verify the efficiency of the methods studied, experiments were carried out on some standard international datasets and comparisons with other methods were made. Also these methods were applied in the pre-processing of data to predict, automatically, the daily temperatures in Camagüey's Meteorologic Center. The results demonstrated that these algorithms can provide efficient solution to find a minimal subset of the features.

• Referencias bibliográficas
  • BELL, D.,WANG, H.. (2000). A Formalism for Relevance and its Application in Feature Subset Selection. Machine Learning.
  • BELLO, R.,NOWÉ, A.,CABALLERO, Y.. (2005). A Model Based on Ant Colony System and Rough Set Theory to Feature Selection.
  • BLUM, A.,LANGLEY, P.. (1997). Selection of Relevant Features and Examples in Machine Learning.
  • BREIMAN, L.,FRIEDMAN, J.,OLSHEN, R.,STONE, C.. (1984). Classification and Regresion Trees.
  • CHEN, Y.,MIAO, D.,WANG, R.. (2010). A Rough set Approach to Feature Selection Based on Ant Colony Optimization. Pattern Recognition Letters....
  • CHOUBEY, S. K.. (1996). A Comparison of Feature Selection Algorithms in the Context of Rough Classifiers.
  • CHOUCHOULAS, A.,SHEN, Q.. (1999). A Rough Set-Based Approach to Text Classification. 118-127
  • DAVIES, S.,RUSSELL, S.. (1994). Np-Completeness of Searches for Smallest Possible Feature Sets.
  • DORIGO, M.. (1992). Optimization, Learning and Natural Algorithms.
  • DORIGO, M.,MANIEZZO, V.,COLORNI, A.. (1996). The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. Syst. Man Cybernet....
  • DUDANI, S.. (1975). The Distance-Weighted k-Nearest-Neighbor Rule.
  • DÜNTSCH, I.,GEDIGA, G.. (2000). ROUGH Set Data Analysis: A Road to Non-Invasive Knowledge Discovery. Methodos Publishers.
  • GÓMEZ, M. Y.. (2010). Algoritmos que combinan conjuntos aproximados y optimización basada en colonias de hormigas para la selección de rasgos....
  • GRABOWSKI, A.. (2003). Basic Properties of Rough Sets and Rough Membership Function. Journal of Formalized Mathematics. 15.
  • GRECO, S.,INUIGUCHI, M.. (2003). Rough Sets and Gradual Decision Rules. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing.
  • GRZYMALA-BUSSE, J. W.,Siddhaye, S.. (2004). Rough Set Approaches to Rule Induction from Incomplete Data.
  • HALL, M.. (1999). Correlation-Based Feature Selection for Machine Learning. University of Waikato. Hamilton.
  • JENSEN, R.,SHEN, Q.. (2003). Finding Rough Set Reducts with Ant Colony Optimization.
  • KOHAVI, R.,FRASCA, B.. (1994). Useful Feature Subsets and Rough Set Reducts.
  • KOHAVI, R.,JOHN, G.. (1997). Wrappers for Feature Subset Selection. Artifcial Intelligence.
  • KOLLER, D.,SAHAMI, M.. (1996). Toward Optimal Feature Selection.
  • KUDO, M.,SOMOL, P.,PUDIL, P.,SHIMBO, M.,SKLANSKY, J.. (2000). Comparison of Classifier Specific Feature Selection Algorithms. 677-686
  • LECHA, L.,FLORIDO, A.. (1989). Principales características climáticas del régimen térmico del archipiélago cubano.
  • LIU, H.,MOTODA, H.. (2007). Computational Methods of Feature Selection.
  • LIU, H.,YU, L.. (2002). Feature Selection for data Mining (Technical report). Arizona State University.
  • LIU, H.,YU, L.. (2005). Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Trans.
  • MIAO, D.,HOU, L.. (2003). An Application of Rough Sets to Monk's Problems Solving Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing....
  • MIDELFART, H.,KOMOROWSKI, J.. (2003). Learning Rough Set Classifiers from Gene Expression and Clinical data. Fundamenta Informaticae. 53....
  • MING, H.. (2008). Feature Selection Based on Ant Colony Optimization and Rough Set Theory.
  • PARSONS, S.. (2006). Current Approaches to Handling Imperfect Information in Data and Knowledges Bases.
  • PAWLAK, Z.. (1982). Sets. International Journal of Computer and Information Sciences. 11. 341-356
  • PIÑERO, P.,ARCO, L.. (2003). Two New Metrics for Feature Selection in Pattern Recognition. 488-497
  • RUIZ, D. R.. (2005). Selección de Atributos mediante proyecciones. Universidad de Sevilla. Sevilla.
  • SIEDLECKI, W.,SKLANSKY, J.. (1988). On Automatic Feature Selection. Int. Journal of Pattern Recognition and Artifcial Intelligence. 2. 197-220
  • SKOWRON, A.. (1999). New Directions in Rough Sets.
  • SKOWRON, A.,PETERS, J. F.. (2003). Rough Sets: Trends and Challenges.
  • SUGIHARA, K.,TANAKA, H.. (2006). Rough Sets Approach to Information Systems with Interval Decision Values in Evaluation Problems.
  • TSUMOTO, S.. (2003). Automated Extraction of Hierarchical Decision Rules from Clinical Databases Using Rough Set Model. Expert systems with...
  • YANG, J.,HONAVAR, V.. (1998). Feature Extraction, Construction and Selection. In K. A. Publishers.
  • ZHAO, Y.,ZHANG, H.. (2003). Classification Using the Variable Precision Rough Set. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing....