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Cost-sensitive classifier ensemble for medical decision support system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Multiple classifier systems are currently the focus of intense research. In this conceptual approach, the main effort focuses on establishing decision on the basis of a set of individual classifiers' outputs. This approach is well known but usually most of propositions do not take exploitation cost of such a classifier under consideration. The paper deals with the problem how to take a test acquisition cost during classification task under the framework of combined approach on board. The problem is known as cost-sensitive classification and it has been usually considered for the decision tree induction. In this work we adapt mentioned above idea into choosing members of classifier ensemble and propose a method of choosing a pool of individual classifiers which take into consideration on the one hand quality of ensemble on the other hand cost of classification. Properties of mentioned concept are established during computer experiments conducted on chosen medical benchmark databases from UCI Machine Learning Repository.
Rocznik
Tom
Strony
97--104
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
  • Department of Systems and Computer Networks, Wroclaw University of Technology, Wyb.Wyspianskiego 27, 50-370 Wroclaw, Poland
autor
Bibliografia
  • [1] ALEXANDRE L.A., CAMPILHO A.C., KAMEL M., Combining Independent and Unbiased Classifiers Using Weighted Average, Proc. of the 15th Internat. Conf. on Pattern Recognition, Vol.2, 2000, pp. 495-498.
  • [2] ASUNCION A., NEWMAN D.J., UCI ML Repository, Irvine, CA: University of California, School of Information and Computer Science, 2007, http://www.ics.uci.edu/~mlearn/MLRepository.html.
  • [3] CHOW C.K., Statistical independence and threshold functions, IEEE Trans. on Electronic Computers, EC-16, 1965, pp. 66-68.
  • [4] DIETTERICH T.G., BAKIRI G., Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research, 2, 1995, pp. 263-286.
  • [5] DUDA R.O., et al., Pattern Classification, Wiley-Interscience, 2001.
  • [6] GIACINTO G., Design Multiple Classifier Systems, PhD thesis, Universita Degli Studi di Salerno, 1998.
  • [7] GREINER R., GROVE A., ROTH D., Learning active classifiers, Proceedings of the 13th International Conference on Machine Learning, 1996, pp.207-215, 1996.
  • [8] HANSEN L.K., SALAMON P. , Neural Networks Ensembles, IEEE Trans. on PAMI, 12(10), 1990, pp. 993-1001.
  • [9] JAIN A.K., DUIN P.W., MAO J., Statistical Pattern Recognition: A Review, IEEE Trans. on PAMI, 22(1), 2000, pp. 4-37.
  • [10] KUNCHEVA L.I., Combining pattern classifiers: Methods and algorithms, Wiley, 2004.
  • [11] LIROV, Y., YUE, O.C., Automated network troubleshooting knowledge acquisition, Journal of Applied Intelligence, 1, 1991, pp. 121-132.
  • [12] MARCIALIS G.L., ROLI F., Fusion of Face Recognition Algorithms for Video-Based Surveillance Systems, in FORESTI G.L., REGAZZONI C., VARSHNEY P., (eds.), Multisensor Surveillance Systems: The Fusion Perspective, Kluwer Academic Pub., 2003.
  • [13] MICHALEWICZ Z., Genetics Algorithms + Data Structures = Evolutions Programs, Springer-Verlag, Berlin 1996.
  • [14] NUNEZ, M., Economic induction: A case study, Proceedings of the Third European Working Session on Learning EWSL-88, California: Morgan Kaufmann, 1998, pp. 139-145.
  • [15] NUNEZ, M., The use of background knowledge in decision tree induction, Machine Learning, 6, 1991, pp. 231-250.
  • [16] TAN M., SCHLIMMER J., Cost-sensitive concept learning of sensor use in approach and recognition, Proceedings of the Sixth International Workshop on Machine Learning ML-89, Ithaca, New York, 1989, pp. 392—395.
  • [17] TUMER, K., GHOSH, J., Analysis of Decision Boundaries in Linearly Combined Neural Classifiers, Pattern Recognition, 29, 1996, pp. 341–348.
  • [18] TURNEY P.D., Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, J. Artif. Intell. Res., 2, 1995, pp. 369-409.
  • [19] Van der HEIJDEN, F., DUIN, R.P.W., de RIDDER, D., TAX D.M.J., Classification, parameter estimation and state estimation - an engineering approach using Matlab, John Wiley and Sons, 2004.
  • [20] Van ERP M., VUURPIJL L.G., SCHOMAKER L.R.B., An overview and comparison of voting methods for pattern recognition, Proc. of IWFHR.8, Canada, 2002, pp. 195–200.
  • [21] VERDENIUS, F., A method for inductive cost optimization, Proceedings of the Fifth European Working Session on Learning EWSL-91, New York: Springer-Verlag, 1991, pp. 179-191.
  • [22] WOLPERT D.H., The supervised learning no-free-lunch theorems, Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0016-0010
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