Journal of Geology & Geophysics
Open Access

Syamsiah Mashohor

Publications

• Research Article
  Pruned Committee Neural Network Based on Accuracy and Diversity Trade-off for Permeability Prediction
  Author(s): Seyed Ali Jafari Kenari and Syamsiah Mashohor Seyed Ali Jafari Kenari and Syamsiah Mashohor
  
  Committee Machine (CM) or ensemble introduces a machine learning technique that aggregates some learners or experts to improve generalization performance compared to single member. The constructed CMs are sometimes unnecessarily large and have some drawbacks such as using extra memories, computational overhead, and occasional decrease in effectiveness. Pruning some members of this committee while preserving a high diversity among the individual experts is an efficient technique to increase the predictive performance. The diversity between committee members is a very important measurement parameter which is not necessarily independent of their accuracy and essentially there is a tradeoff between them. In this paper, first we constructed a committee neural network with different learning algorithms and then proposed an expert pruning method based on diversity and accuracy tradeoff to im.. View More»
  DOI: 10.4172/2329-6755.1000144

  Abstract PDF

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