Paper Title:
A Subgraph-Based Selective Classifier Ensemble Algorithm
  Abstract

To improve the generalization performance for ensemble learning, a subgraph based selective classifier ensemble algorithm is presented. Firstly, a set of classifiers are generated by bootstrap sampling technique and support vector machine learning algorithm. And a complete undirected graph is constructed whose vertex is classifier and weight of edge between a pair of classifiers is diversity values. Secondly, by searching technique to find an edge with minimum weight and to calculate similarity values about two vertexes which is related to the edge, vertex with smaller similarity value is removed. According to this method, a subgraph is obtained. Finally, we choose vertexes of subgraph, i.e. classifiers, as ensemble members. Experiments show that presented method outperforms the traditional ensemble learning methods in classification accuracy.

  Info
Periodical
Advanced Materials Research (Volumes 219-220)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
261-264
DOI
10.4028/www.scientific.net/AMR.219-220.261
Citation
K. Li, H. T. Gao, "A Subgraph-Based Selective Classifier Ensemble Algorithm", Advanced Materials Research, Vols. 219-220, pp. 261-264, 2011
Online since
March 2011
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$32.00
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