Accuracy and Diversity in Ensemble Systems Composed of ARTMAP-Based Models

ARTMAP-based models are neural networks which uses a match-based learning procedure. The main advantage of ARTMAP-based models over error-based models, such as Multi-layer Perceptron, is the learning time, which is considered as significantly fast. This feature is extremely important in complex systems that require the use of several neural models, such as ensembles or committees, since they produce strong and fast classifiers. Aiming to add an extra contribution to ARTMAP-based ensembles, this paper presents an analysis of accuracy and diversity in these systems. As a result of this analysis, it is intended to detect any relation between these two parameters and to use this in the design of these systems.

[1]  Terry Windeatt,et al.  Diversity measures for multiple classifier system analysis and design , 2004, Inf. Fusion.

[2]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[3]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[4]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[5]  Ludmila I. Kuncheva,et al.  Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse , 2002, Multiple Classifier Systems.

[6]  Anne M. P. Canuto,et al.  Investigating the influence of RePART in ensemble systems designed by boosting , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[7]  A. M. de Paula Canuto,et al.  A comparative investigation of the RePART neural network in pattern recognition tasks , 2004 .

[8]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[9]  Gail A. Carpenter,et al.  ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases , 1998, Neural Networks.

[10]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[11]  Anne M. P. Canuto,et al.  Improving Artmap Learning Through Variable Vigilance , 2001, Int. J. Neural Syst..

[12]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[13]  Anne M. P. Canuto,et al.  An Investigation of the Effects of Variable Vigilance within the RePART Neuro-Fuzzy Network , 2000, J. Intell. Robotic Syst..

[14]  Robert P. W. Duin,et al.  An experimental study on diversity for bagging and boosting with linear classifiers , 2002, Inf. Fusion.

[15]  Xin Yao,et al.  An analysis of diversity measures , 2006, Machine Learning.

[16]  Anne M. P. Canuto,et al.  Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles , 2007, Pattern Recognit. Lett..

[17]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.