A COMPARISON STUDY OF TWO KNOWLEDGE ACQUISITION TECHNIQUES APPLIED TO THYROID MEDICAL DIAGNOSIS DOMAIN

Abdulhamed Mohamed Abdulkafi, Aiman Subhi Gannous

2008

Abstract

This study compares the performance of two famous methods used in knowledge acquisition and machine learning; the C4.5 (Quinlan 1986) algorithm for building the decision tree and the Backpropagation algorithm for training Multi layer feed forward neural network. This comparison will be based on the task of classifying thyroid diagnosis dataset. Both methods will be applied on the same data set and then study and discuss the results obtained from the experiments.

References

  1. Leiva, H., 2002. A multi-relational decision tree learning algorithm ,Msc, Iowa State University Ames.
  2. Thomas, D., Hild, H., and Ghulum Bakiri, G., 1995. A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping Machine Learning, Kluwer Academic Publishers, Boston.
  3. Berkman, S., Lubomir, H., Ping, C., Chuan, Z., Wei Jun, W,. 2005. Comparison of decision tree classifiers with neural network and linear discriminant analysis classifiers for computer-aided diagnosis: a Monte Carlo simulation study, Medical Imaging: Image Processing, Volume 5747.
  4. Bagnall, A., Cawley, G., 2000. Learning Classifier Systems for Data Mining: A Comparison of XCS with Other Classifiers for the Forest Cover Data Set, University of East Anglia, England.
  5. The UCI KDD archive. Irvine, University of California, Department of Information and Computer Science, http://kdd.ics.uci.edu. Last access September 2007.
  6. Mitra, S., Tinkuacharya ., 2003, Data mining multimedia, soft computing, and Bioinformatics, John Wiley & Sons, Inc.
  7. Ye, N., 2003. Hand book of data mining, Arizona State University, Lawrence Erlbaum Associates, Inc, New Jersey.
  8. Kantardzic, M., 2003. Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons.Inc.
  9. Larose, D., 2005, Discovering Knowledge in data, an introduction to data mining, John Wiley & Sons.Inc.
  10. Michael, A., Berry, S., 2004. Data mining Techniques, John Wiley & Sons.Inc, 2nd edition.
  11. Paplinski, A., 2004. Basic structures and properties of Artificial Neural Networks.retrivedfrom: lsc.fie.umich.mx/juan/Materias/ANN/PublishedPapers/basic structures-and-properties.pdf, last access June 2006.
  12. Zurada, J., 1992. Introduction to Artificial Neural Systems, West publishing Company, Singapore.
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Paper Citation


in Harvard Style

Mohamed Abdulkafi A. and Subhi Gannous A. (2008). A COMPARISON STUDY OF TWO KNOWLEDGE ACQUISITION TECHNIQUES APPLIED TO THYROID MEDICAL DIAGNOSIS DOMAIN . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8111-51-7, pages 360-365. DOI: 10.5220/0001880503600365


in Bibtex Style

@conference{icsoft08,
author={Abdulhamed Mohamed Abdulkafi and Aiman Subhi Gannous},
title={A COMPARISON STUDY OF TWO KNOWLEDGE ACQUISITION TECHNIQUES APPLIED TO THYROID MEDICAL DIAGNOSIS DOMAIN},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,},
year={2008},
pages={360-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001880503600365},
isbn={978-989-8111-51-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,
TI - A COMPARISON STUDY OF TWO KNOWLEDGE ACQUISITION TECHNIQUES APPLIED TO THYROID MEDICAL DIAGNOSIS DOMAIN
SN - 978-989-8111-51-7
AU - Mohamed Abdulkafi A.
AU - Subhi Gannous A.
PY - 2008
SP - 360
EP - 365
DO - 10.5220/0001880503600365