Instance Selection in the Performance of Gamma Associative Classifier

The Gamma associative classifier is among the most used classifiers of the alpha-beta associative approach. It had been used successfully to solve many Pattern Recognition tasks, including environmental applications. However, as most classifiers, Gamma suffers with the presence of noisy or mislabeled instances in the training sets. This paper evaluates the impact of using instance selection techniques in the performance of Gamma classifier. The numerical experiments carried out over well-known repository datasets allows to conclude that instance selection may increase the testing accuracy of the Gamma classifier.

[1]  José Ruiz-Shulcloper,et al.  Object Selection Based on Subclass Error Correcting for ALVOT , 2007, CIARP.

[2]  Rafael Bello,et al.  A Method to Edit Training Set Based on Rough Sets , 2007 .

[3]  Sung-Bae Cho,et al.  A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets , 2010, Neural Computing and Applications.

[4]  Yenny Villuendas,et al.  Evolutive Improvement of Parameters in an Associative Classifier , 2015, IEEE Latin America Transactions.

[5]  Cornelio Yáñez-Márquez,et al.  Analysis and Prediction of Air Quality Data with the Gamma Classifier , 2008, CIARP.

[6]  José Ruiz-Shulcloper,et al.  Selecting Objects for ALVOT , 2006, CIARP.

[7]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[8]  Cornelio Yáñez-Márquez,et al.  Memorias Asociativas Basadas en Relaciones de Orden y Operaciones Binarias , 2003, Computación y Sistemas.

[9]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[10]  Godfried T. Toussaint,et al.  Proximity Graphs for Nearest Neighbor Decision Rules: Recent Progress , 2002 .

[11]  Itzama Lopez Yanez.  CLASIFICADOR AUTOMATICO DE ALTO DESEMPENO , 2008 .

[12]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[13]  Francisco Herrera,et al.  A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Francisco Herrera,et al.  Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  José Francisco Martínez Trinidad,et al.  Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers , 2009, CIARP.

[16]  Oscar Camacho Nieto,et al.  Pollutants Time-Series Prediction using the Gamma Classifier , 2011, Int. J. Comput. Intell. Syst..

[17]  Cornelio Yáñez-Márquez,et al.  A novel associative model for time series data mining , 2014, Pattern Recognit. Lett..

[18]  Hisao Ishibuchi,et al.  Learning of neural networks with GA-based instance selection , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[19]  Cornelio Yáñez-Márquez,et al.  Application of Gamma Classifier to Development Effort Prediction of Software Projects , 2012 .