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Problems of medical data mining

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article discusses the main problems connected to the specificity of medical aspects, especially as concerns the quality and means of selection of data and tools used for constructing classification systems. Special attention is devoted to the risks inherent in direct application of classical knowledge extraction algorithms (such as the algorithms for constructing decision trees) to medical data. The article describes some attempts at solving emerging problems and points to the need for analysis of classifiers with regard to more than just their potential redundancy and mutual exclusion. The article also proposes two functions, useful for analysing rule sets with focus on data semantics.
Rocznik
Tom
Strony
KB91--98
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
  • Department of Computer Science, Wroclaw University of Technology
Bibliografia
  • [1] BING LIU, MINQUING HU, WYNNE HSU, Multi-Level Organization and Summarization of the Discovered Rules, KDD-2000, 2000
  • [2] CATLETT J., LEWIS D., Heterogeneous uncertainty sampling for supervised learning. In 11th International Conference on Machine Learning, 1994.
  • [3] CIOS K.J., MOORE G.W., Uniqueness of medical data mining, Artificial Intelligence in Medicine, Vol. 26, No 1-2,pp.1-24, September-October 2002
  • [4] DREWRY D. T., LIN GU, HOCKING A. B., KYOUNG-DON KANG, PFALTZ J. L., SCHUTT R. C., TAYLOR C. M., Current state of data mining, University of Virginia, Department of Computer Science, Technical Report,2002
  • [5] FREITAS A.A.: On rule interestingness measures. Knowledge-Based Systems journal 12 (5-6), 309-315. Oct. 1999.
  • [6] GAMBERGER D., LAVRAC N., Conditions for Occam's razor applicability and noise elimination. Proc. 9th European Conference on Machine Learning , pp. 108-123, Springer, Berlin,19997
  • [7] GAMBERGER D., LAVRAC N., KRSTATIC G.,SMUC T., Inconsistency tests for patients records in a coronary heart disease database, Proc. of Int. Symp. on Medical Data Analysis, pp. 183-189,2000
  • [8] GAMBERGER D., LAVRAC N., GROSELI C., Experiments with noise filtering in a medical domain. Proc. International Conference on Machine Learning, pp. 143-151,1999
  • [9] HARRIES M. B., SAMMUT C., HORN K., Extracting hidden context. Machine Learning, Vol 32,pp. 101-126, 1998
  • [10] HILDERMAN R, HAMILTON H., BARBER B., Ranking the interestingness of summaries from data mining systems, FLAIRS'99, Orlando, FL, May 1999
  • [11] KUKAR M., KONONENKO I., GROSELJ C., KRALJ K., FETTICH J., Analysing and improving the diagnosis of ischaemic heart disease with machine learning, Artificial Intelligence in Medicine, Vol 16,pp. 25-50, 1999
  • [12] LINDENBAUM M., MARKOVITCH S., RUSAKOV D., Selective Sampling for Nearest Neighbour Classifiers, Machine Learning, Volume 54, 2004
  • [13] LIU H., MOTODA H., YU L. Feature selection with selective sampling. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 395 - 402, 2002
  • [14] SHAHAR Y., CHENG C., Knowledge-Based Visualization of Time-Oriented Clinical Data. Proceedings of the 1998 AMIA Fall Symposium, Orlando, FL1998
  • [15] SIMINSKI R., WAKULICZ-DEJA A., Lokalna i globalna weryfikacja regulowych baz wiedzy w oparciu o koncepcje jednostek decyzyjnych, Mater. III Konf. MSK, Kraków, 2001
  • [16] THRUN S., Exploration in active learning. In M. A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 381-384. MIT Press, Cambridge, MA, 1995.
  • [17] WEBB G. I., Decision tree grafting, Fifteenth International Joint Conference on Artificial Intelligence, pp. 846--851, Morgan Kaufmann, Japan, 1997.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0013-0013
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