Combining classifiers - concept and applications

• Autorzy: Woźniak M.
• Języki publikacji: EN

Abstrakty

EN

Problem of pattern recognition is accompanying our whole life, therefore methods of automatic pattern recognition is one of the main trend in Artificial Intelligence. Multiple classifier systems (MCSs) are currently the focus of intense research. In this conceptual approach, the main effort is concentrated on combining knowledge of the set of individual classifiers. Proposed work presents a brief survey of the main issues connected with MCSs and provides comparative analysis of some classifier fusion methods.

Słowa kluczowe

EN

combining classifier; multiple classifier systems; pattern recognition; fuser design; ensemble design

PL

łączenie klasyfikatorów; systemy wielo-klasyfikatorów; rozpoznawanie obrazów; projekt nagrzewnicy; projekt zespołu

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 15
• Strony: 19--27
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Woźniak M.

• Department of Systems and Computer Networks, Wroclaw University of Technology, Wyb.Wyspianskiego 27, 50-370 Wroclaw, Poland

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 [http://www.ics.uci.edu/~mlearn/MLRepository.html], Irvine, CA: University of California, School of Information and Computer Science.
• [3] BIGGIO B., FUMERA G., ROLI F., Bayesian Analysis of Linear Combiners, LNCS, Vol. 4472, 2007, pp. 292–301.
• [4] BISHOP Ch.M., Pattern Recognition and Machine Learning, Springer, 2006.
• [5] CHOW C.K., Statistical independence and threshold functions, IEEE Trans. on Electronic Computers, EC–16, 1965, pp. 66–68.
• [6] DIETTERICH T.G., BAKIRI G., Solving multiclass learning problems via error–correcting output codes, Journal of Artificial Intelligence Research, 2, 1995, pp. 263–286.
• [7] DUDA R.O., et al., Pattern Classification, Wiley–Interscience, 2001.
• [8] DUIN R.P.W., TAX, D.M.J., Experiments with Classifier Combining Rules, LNCS, No. 1857, 2000, pp. 16–29.
• [9] DUIN R. P.W., The Combining Classifier: to Train or Not to Train?, Proc. of the ICPR2002, Quebec City, 2002.
• [10] FUMERA G., ROLI F., A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems, IEEE Trans.on PAMI, 27(6), 2005, pp. 942–956.
• [11] GIACINTO G. , Design Multiple Classifier Systems, PhD thesis, Universita Degli Studi di Salerno, 1998.
• [12] HANSEN L.K., SALAMON P. , Neural Networks Ensembles, IEEE Trans. on PAMI, Vol. 12, No. 10, 1990, pp. 993–1001.
• [13] HASHEM S., Optimal linear combinations of neural networks, Neural Networks, 10(4), 1997, pp. 599–614.
• [14] INOUE H., NARIHISA H., Optimizing a Multiple Classifier Systems, LNCS, Vol. 2417, 2002, pp. 285–294.
• [15] JAIN A.K., DUIN P.W., MAO J., Statistical Pattern Recognition: A Review, IEEE Trans. on PAMI, vol 22., No. 1, 2000, pp. 4–37.
• [16] JACKOBS R.A., Methods for combining experts’ probability assessment, Neural Computation, No. 7, 1995, pp. 867–888.
• [17] KITTLER J., ALKOOT F.M., Sum versus Vote Fusion in Multiple Classifier Systems, IEEE Trans. on Pattern Analysis and Machine Intelligence, 20, 2003, pp. 226–239.
• [18] KRZANOWSKI W., PARTRIGE D., Software Diversity: Practical Statistics for its Measurement and Exploitation, Department of Computer Science, University of Exeter, 1996.
• [19] KUNCHEVA L.I., WHITAKER C.J., SHIPP C.A., DUIN R.P.W., Limits on the Majority Vote Accuracy in Classier Fusion, Pattern Analysis and Applications, 6, 2003, pp. 22–31.
• [20] KUNCHEVA L.I., Combining pattern classifiers: Methods and algorithms, Wiley, 2004.
• [21] 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.
• [22] POLIKAR R., Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, 3rd quarter, 2006, pp. 21–45.
• [23] RAO N.S.V., A Generic Sensor Fusion Problem: Classification and Function Estimation, LNCS, Vol. 3077, 2006, pp. 16–30.
• [24] RAUDYS S., Trainable fusion rules. I. Large sample size case, Neural Networks 19, 2006, pp. 1506–1516.
• [25] RAUDYS S., Trainable fusion rules. II. Small sample–size effects, Neural Networks 19, 2006, pp. 1517–1527.
• [26] TUMER K., GHOSH J., Analysis of Decision Boundaries in Linearly Combined Neural Classifiers, Pattern Recognition, 29, 1996, pp. 341–348.
• [27] 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.
• [28] 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.
• [29] WOLPERT D.H., The supervised learning no–free–lunch theorems. In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, 2001.
• [30] WOODS K., KEGELMEYER W.P., Combination of multiple classifiers using local accuracy estimates, IEEE Transactions on PAMI, Vol. 19, Issue 4, 1997, pp. 405–410.
• [31] WOZNIAK M., Experiments with trained and untrained fusers [in:] Corchado E. et al. (eds) Innovations in hybrid intelligent systems, Springer series “Advances in Soft Computing”, Berlin, 2007, pp. 144–150.
• [32] WOZNIAK M., JACKOWSKI K., Some remarks on chosen methods of classifier fusion based on weighted voting, LNCS Vol. 5572, 2009, pp. 541–548.
• [33] WOZNIAK M., Evolutionary approach to produce classifier ensemble based on weighted voting, Prodeedings of World Congress on Nature & Biologically Inspired Computing, NaBIC' 2009 , 9–11 December 2009, Coimbatore, India, 2009, pp. 648–653.
• [34] WOZNIAK M., ZMYSLONY M., Fuser on the basis of discriminants evolutionary and neural methods of training, LNCS Vol. 6077, Springer, 2010, pp. 590–597.
• [35] WOZNIAK M., ZMYSLONY M. , Method of designing classifier fuser – evolutionary approach, Proceedings of the 44th Spring International Conference Modelling and simulation of systems, Ostrava, 2010, pp. 88–92.
• [36] XU L., KRZYZAK A., SUEN Ch.Y., Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition, IEEE Trans. on SMC, No. 3, 1992, pp. 418–435.