A New Artificial Neural Network Ensemble Based on Feature Selection and Class Recoding Articles uri icon

publication date

  • October 2010

International Standard Serial Number (ISSN)

  • 0941-0643

Electronic International Standard Serial Number (EISSN)

  • 1433-3058


  • Many of the studies related to supervised learning have focused on the resolution of multiclass problems. A standard technique used to resolve these problems is to decompose the original multiclass problem into multiple binary problems. In this paper, we propose a new learning model applicable to multi-class domains in which the examples are described by a large number of features. The proposed model is an Artificial Neural Network ensemble in which the base learners are composed by the union of a binary classifier and a multiclass classifier. To analyze the viability and quality of this system, it will be validated in two real domains: traffic sign recognition and hand-written digit recognition. Experimental results show that our model is at least as accurate as other methods reported in the bibliography but has a considerable advantage respecting size, computational complexity, and running time.