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Handbook on Neural Information Processing pp 335–378Cite as

Multiple Classifier Systems: Theory, Applications and Tools

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 49))

Abstract

In many Pattern Recognition applications, the achievement of acceptable recognition rates is conditioned by the large pattern variability, whose distribution cannot be simply modeled.

This affects the results at each stage of the recognition system so that, once this has been designed, its performance cannot be improved over a certain bound, despite the efforts in refining either the classification or the description method.

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References

  1. International workshop on multiple classifier systems. Web Page, http://www.diee.unica.it/mcs/

  2. Ahmad, A., Brown, G.: A study of random linear oracle ensembles. In: Benediktsson, et al. [12], pp. 488–497

    Google Scholar 

  3. Alam, H., Rahman, A.F.R., Tarnikova, Y.: Solving problems two at a time: Classification of web pages using a generic pair-wise multiple classifier system. In: Windeatt, Roli [134], pp. 385–394

    Google Scholar 

  4. Alpaydin, E.: Introduction To Machine Learning. MIT Press (2004)

    Google Scholar 

  5. Ariu, D., Giacinto, G.: A modular architecture for the analysis of http payloads based on multiple classifiers. In: Sansone, et al. [112], pp. 330–339

    Google Scholar 

  6. Asdornwised, W., Jitapunkul, S.: Automatic target recognition using multiple description coding models for multiple classifier systems. In: Windeatt, Roli [134], pp. 336–345

    Google Scholar 

  7. Azizi, N., Farah, N., Sellami, M., Ennaji, A.: Using diversity in classifier set selection for arabic handwritten recognition. In: Gayar, et al. [52], pp. 235–244

    Google Scholar 

  8. Azmy, W.M., Atiya, A.F., El-Shishiny, H.: Forecast combination strategies for handling structural breaks for time series forecasting. In: Gayar, et al. [52], pp. 245–253.

    Google Scholar 

  9. Azmy, W.M., El Gayar, N., Atiya, A.F., El-Shishiny, H.: Mlp, gaussian processes and negative correlation learning for time series prediction. In: Benediktsson, et al. [12], pp. 428–437

    Google Scholar 

  10. Batista, L., Granger, E., Sabourin, R.: A multi-classifier system for off-line signature verification based on dissimilarity representation. In: Gayar, et al. [52], pp. 264–273

    Google Scholar 

  11. Batista, L., Granger, E., Sabourin, R.: Dynamic ensemble selection for off-line signature verification. In: Sansone, et al. [112], pp. 157–166

    Google Scholar 

  12. Benediktsson, J.A., Kittler, J., Roli, F. (eds.): MCS 2009. LNCS, vol. 5519. Springer, Heidelberg (2009)

    MATH  Google Scholar 

  13. Benediktsson, J.A., Sveinsson, J.R.: Consensus based classification of multisource remote sensing data. In: Kittler, Roli [73], pp. 280–289

    Google Scholar 

  14. Benfenati, E., Mazzatorta, P., Neagu, D., Gini, G.C.: Combining classifiers of pesticides toxicity through a neuro-fuzzy approach. In: Roli, Kittler [107], pp. 293–303

    Google Scholar 

  15. Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: The konstanz information miner. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) GfKl. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 319–326. Springer (2007)

    Google Scholar 

  16. Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Thiel, K., Wiswedel, B.: KNIME - the konstanz information miner: version 2.0 and beyond. SIGKDD Explorations 11(1), 26–31 (2009)

    Article  Google Scholar 

  17. Bertolami, R., Bunke, H.: Multiple classifier methods for offline handwritten text line recognition. In: Haindl, et al. [59], pp. 72–81

    Google Scholar 

  18. Biggio, B., Corona, I., Fumera, G., Giacinto, G., Roli, F.: Bagging classifiers for fighting poisoning attacks in adversarial classification tasks. In: Sansone, et al. [112], pp. 350–369

    Google Scholar 

  19. Bonissone, P.P., Eklund, N., Goebel, K.: Using an ensemble of classifiers to audit a production classifier. In: Oza, et al. [95], pp. 376–386

    Google Scholar 

  20. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  21. Breiman, L., Breiman, L.: Bagging predictors. Machine Learning, 123–140 (1996)

    Google Scholar 

  22. Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.: Boosting, bagging, and consensus based classification of multisource remote sensing data. In: Kittler, Roli [74], pp. 279–288

    Google Scholar 

  23. Bruzzone, L., Cossu, R.: A robust multiple classifier system for a partially unsupervised updating of land-cover maps. In: Kittler, Roli [74], pp. 259–268

    Google Scholar 

  24. Bruzzone, L., Cossu, R., Prieto, D.F.: Combining parametric and nonparametric classifiers for an unsupervised updating of land-cover maps. In: Kittler, Roli [73], pp. 290–299

    Google Scholar 

  25. Cappelli, R., Maio, D., Maltoni, D.: Combining fingerprint classifiers. In: Kittler, Roli [73], pp. 351–361

    Google Scholar 

  26. Chawla, N.V., Bowyer, K.W.: Designing multiple classifier systems for face recognition. In: Oza, et al. [95], pp. 407–416

    Google Scholar 

  27. Chindaro, S., Sirlantzis, K., Fairhurst, M.C.: Analysis and modelling of diversity contribution to ensemble-based texture recognition performance. In: Oza, et al. [95], pp. 387–396

    Google Scholar 

  28. Christensen, H.U., Arroyo, D.O.: Applying data fusion methods to passage retrieval in qas. In: Haindl, et al. [59], pp. 82–92

    Google Scholar 

  29. Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M.: A cascaded multiple expert system for verification. In: Kittler, Roli [73], pp. 330–339

    Google Scholar 

  30. Cordella, L.P., Limongiello, A., Sansone, C.: Network intrusion detection by a multi-stage classification system. In: Roli, et al. [109], pp. 324–333

    Google Scholar 

  31. Cordella, L.P., De Santo, M., Percannella, G., Sansone, C., Vento, M.: A multi-expert system for movie segmentation. In: Roli, Kittler [107], pp. 304–313

    Google Scholar 

  32. Csirik, J., Bertholet, P., Bunke, H.: Sequential classifier combination for pattern recognition in wireless sensor networks. In: Sansone, et al. [112], pp. 187–196

    Google Scholar 

  33. Dahmen, J., Keysers, D., Ney, H.: Combined classification of handwritten digits using the ’virtual test sample method’. In: Kittler, Roli [74], pp. 109–118

    Google Scholar 

  34. Dainotti, A., Pescapè, A., Sansone, C., Quintavalle, A.: Using a behaviour knowledge space approach for detecting unknown ip traffic flows. In: Sansone, et al. [112], pp. 360–369

    Google Scholar 

  35. de Borda, J.-C.: Memoire sur les elections au scrutin. Memoires de l’Academie Royale des Sciences, 657–664 (1781)

    Google Scholar 

  36. Degtyarev, N., Seredin, O.: A geometric approach to face detector combining. In: Sansone, et al. [112], pp. 299–308

    Google Scholar 

  37. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, Roli [73], pp. 1–15

    Google Scholar 

  38. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)

    MATH  Google Scholar 

  39. Dolenko, S.A., Orlov, Y.V., Persiantsev, I.G., Shugai, J.S., Dmitriev, A.V., Suvorova, A.V., Veselovsky, I.S.: Solar wind data analysis using self-organizing hierarchical neural network classifiers. In: Kittler, Roli [74], pp. 289–298

    Google Scholar 

  40. Du, P., Li, G., Zhang, W., Wang, X., Sun, H.: Consistency measure of multiple classifiers for land cover classification by remote sensing image. In: Benediktsson, et al. [12], pp. 398–407

    Google Scholar 

  41. Du, P., Sun, H., Zhang, W.: Target identification from high resolution remote sensing image by combining multiple classifiers. In: Benediktsson, et al. [12], pp. 408–417

    Google Scholar 

  42. Duin, R.P.W., Juszczak, P., de Ridder, D., Paclík, P., Pekalska, E., Tax, D.M.J.: PR-Tools 4.0, a Matlab toolbox for pattern recognition (2004), http://www.prtools.org

  43. Ebrahimpour, R., Kabir, E., Yousefi, M.R.: View-based eigenspaces with mixture of experts for view-independent face recognition. In: Haindl, et al. [59], pp. 131–140

    Google Scholar 

  44. Erdogan, H., Erçil, A., Ekenel, H.K., Bilgin, S.Y., Eden, I., Kirisçi, M., Abut, H.: Multi-modal person recognition for vehicular applications. In: Oza, et al. [95], pp. 366–375

    Google Scholar 

  45. Fanelli, A.M., Castellano, G., Buscicchio, C.A.: A modular neuro-fuzzy network for musical instruments classification. In: Kittler, Roli [73], pp. 372–382

    Google Scholar 

  46. Fiérrez-Aguilar, J., Garcia-Romero, D., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Speaker verification using adapted user-dependent multilevel fusion. In: Oza, et al. [95], pp. 356–365

    Google Scholar 

  47. Foggia, P., Sansone, C., Tortorella, F., Vento, M.: Automatic classification of clustered microcalcifications by a multiple classifier system. In: Kittler, Roli [74], pp. 208–217

    Google Scholar 

  48. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 13th International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann (1996)

    Google Scholar 

  49. Frinken, V., Fischer, A., Bunke, H.: Combining neural networks to improve performance of handwritten keyword spotting. In: Gayar, et al. [52], pp. 215–224

    Google Scholar 

  50. Fröba, B., Rothe, C., Küblbeck, C.: Statistical sensor calibration for fusion of different classifiers in a biometric person recognition framework. In: Kittler, Roli [73], pp. 362–371

    Google Scholar 

  51. Fröba, B., Zink, W.: On the combination of different template matching strategies for fast face detection. In: Kittler, Roli [74], pp. 418–428

    Google Scholar 

  52. El Gayar, N., Kittler, J., Roli, F. (eds.): MCS 2010. LNCS, vol. 5997. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  53. Giacinto, G., Roli, F., Didaci, L.: A modular multiple classifier system for the detection of intrusions in computer networks. In: Windeatt, Roli [134], pp. 346–355

    Google Scholar 

  54. Gini, G.C., Lorenzini, M., Benfenati, E., Brambilla, R., Malvé, L.: Mixing a symbolic and a subsymbolic expert to improve carcinogenicity prediction of aromatic compounds. In: Kittler, Roli [74], pp. 126–135

    Google Scholar 

  55. Gordon, J., Shortliffe, E.H.: The dempster-shafer theory of evidence. In: Buchanan, B.G., Shortliffe, E.H. (eds.) Rule-Based Expert Systems, pp. 272–292. Addison Wesley Publishing Company, Reading (1984)

    Google Scholar 

  56. Günter, S., Bunke, H.: New boosting algorithms for classification problems with large number of classes applied to a handwritten word recognition task. In: Windeatt, Roli [134], pp. 326–335

    Google Scholar 

  57. Günter, S., Bunke, H.: Ensembles of classifiers derived from multiple prototypes and their application to handwriting recognition. In: Roli, et al. [109], pp. 314–323

    Google Scholar 

  58. Hady, M.F.A., Schwenker, F.: Combining committee-based semi-supervised and active learning and its application to handwritten digits recognition. In: Gayar, et al. [52], pp. 225–234

    Google Scholar 

  59. Haindl, M., Kittler, J., Roli, F. (eds.): MCS 2007. LNCS, vol. 4472. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  60. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)

    Article  Google Scholar 

  61. Higgins, J.E., Dodd, T.J., Damper, R.I.: Application of multiple classifier techniques to subband speaker identification with an hmm/ann system. In: Kittler, Roli [74], pp. 369–377

    Google Scholar 

  62. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)

    Article  Google Scholar 

  63. Huang, Y.S., Suen, C.Y.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 90–94 (1995)

    Article  Google Scholar 

  64. Ianakiev, K.G., Govindaraju, V.: Architecture for classifier combination using entropy measures. In: Kittler, Roli [73], pp. 340–350

    Google Scholar 

  65. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)

    Article  Google Scholar 

  66. Jaser, E., Kittler, J., Christmas, W.J.: Building classifier ensembles for automatic sports classification. In: Windeatt, Roli [134], pp. 366–374

    Google Scholar 

  67. Jiang, X., Yu, K., Bunke, H.: Classifier combination for grammar-guided sentence recognition. In: Kittler, Roli [73], pp. 383–392

    Google Scholar 

  68. Khademi, M., Shalmani, M.T.M., Kiapour, M.H., Kiaei, A.A.: Recognizing combinations of facial action units with different intensity using a mixture of hidden markov models and neural network. In: Gayar, et al. [52], pp. 304–313

    Google Scholar 

  69. Khreich, W., Granger, E., Miri, A., Sabourin, R.: Incremental boolean combination of classifiers. In: Sansone, et al. [112], pp. 340–349

    Google Scholar 

  70. Kittler, J., Ballette, M., Czyz, J., Roli, F., Vandendorpe, L.: Decision level fusion of intramodal personal identity verification experts. In: Roli, Kittler [107], pp. 314–324

    Google Scholar 

  71. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)

    Article  Google Scholar 

  72. Kittler, J., Poh, N., Merati, A.: Cohort based approach to multiexpert class verification. In: Sansone, et al. [112], pp. 319–329

    Google Scholar 

  73. Kittler, J., Roli, F. (eds.): MCS 2000. LNCS, vol. 1857. Springer, Heidelberg (2000)

    Google Scholar 

  74. Kittler, J., Roli, F. (eds.): MCS 2001. LNCS, vol. 2096. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  75. Kittler, J., Sadeghi, M.: Physics-based decorrelation of image data for decision level fusion in face verification. In: Roli, et al. [109], pp. 354–363

    Google Scholar 

  76. Ko, A.H.-R., Sabourin, R., de Souza Britto Jr., A.: A new hmm-based ensemble generation method for numeral recognition. In: Haindl, et al. [59], pp. 52–61

    Google Scholar 

  77. Kumar, S., Ghosh, J., Crawford, M.M.: A hierarchical multiclassifier system for hyperspectral data analysis. In: Kittler, Roli [73], pp. 270–279

    Google Scholar 

  78. Kumazawa, I.: Shape matching and extraction by an array of figure-and-ground classifiers. In: Kittler, Roli [73], pp. 393–402

    Google Scholar 

  79. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience (2004)

    Google Scholar 

  80. Lam, L.: Classifier combinations: Implementations and theoretical issues. In: Kittler, Roli [73], pp. 77–86

    Google Scholar 

  81. Di Lecce, V., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., Salzo, A.: A multi-expert system for dynamic signature verification. In: Kittler, Roli [73], pp. 320–329

    Google Scholar 

  82. Li, P., Chan, K.L., Fu, S., Krishnan, S.M.: An abnormal ecg beat detection approach for long-term monitoring of heart patients based on hybrid kernel machine ensemble. In: Oza, et al. [95], pp. 346–355

    Google Scholar 

  83. Lienemann, K., Plötz, T., Fink, G.A.: On the application of svm-ensembles based on adapted random subspace sampling for automatic classification of nmr data. In: Haindl, et al. [59], pp. 42–51

    Google Scholar 

  84. Lienemann, K., Plötz, T., Fink, G.A.: Stacking for ensembles of local experts in metabonomic applications. In: Benediktsson, et al. [12], pp. 498–508

    Google Scholar 

  85. Loog, M., Li, Y., Tax, D.M.J.: Maximum membership scale selection. In: Benediktsson, et al. [12], pp. 468–477

    Google Scholar 

  86. Lu, Y.: Knowledge integration in a multiple classifier system. Appl. Intell. 6(2), 75–86 (1996)

    Article  Google Scholar 

  87. Marasco, E., Johnson, P., Sansone, C., Schuckers, S.: Increase the security of multibiometric systems by incorporating a spoofing detection algorithm in the fusion mechanism. In: Sansone, et al. [112], pp. 309–318

    Google Scholar 

  88. Marcialis, G.L., Roli, F.: High security fingerprint verification by perceptron-based fusion of multiple matchers. In: Roli, et al. [109], pp. 364–373

    Google Scholar 

  89. Marcialis, G.L., Roli, F.: Serial fusion of fingerprint and face matchers. In: Haindl, et al. [59], pp. 151–160

    Google Scholar 

  90. Masulli, F., Pardo, M., Sberveglieri, G., Valentini, G.: Boosting and classification of electronic nose data. In: Roli, Kittler [107], pp. 262–271

    Google Scholar 

  91. Melville, P., Mooney, R.J.: Diverse ensembles for active learning. In: Brodley, C.E. (ed.) ICML. ACM International Conference Proceeding Series, vol. 69. ACM (2004)

    Google Scholar 

  92. Merler, S., Furlanello, C., Larcher, B., Sboner, A.: Tuning cost-sensitive boosting and its application to melanoma diagnosis. In: Kittler, Roli [74], pp. 32–42

    Google Scholar 

  93. Minguillón, J., Tate, A.R., Arús, C., Griffiths, J.R.: Classifier combination for in vivo magnetic resonance spectra of brain tumours. In: Roli, Kittler [107], pp. 282–292

    Google Scholar 

  94. Mohamed, T.A., El Gayar, N., Atiya, A.F.: A co-training approach for time series prediction with missing data. In: Haindl et al. [59], pp. 93–102

    Google Scholar 

  95. Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.): MCS 2005. LNCS, vol. 3541. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  96. Oza, N.C., Tumer, K., Tumer, I.Y., Huff, E.M.: Classification of aircraft maneuvers for fault detection. In: Windeatt, Roli [134], pp. 375–384

    Google Scholar 

  97. Powalka, R.K., Sherkat, N., Whitrow, R.J.: Multiple recognizer combination topologies. In: Simner, M.L., Leedham, C.G., Thomassen, A.J.W.M. (eds.) Handwriting and Drawing Research: Basic and Applied Issues. IOS Press (1995)

    Google Scholar 

  98. Prabhakar, S., Jain, A.K.: Decision-level fusion in fingerprint verification. In: Kittler, Roli [74], pp. 88–98

    Google Scholar 

  99. Pranckeviciene, E., Baumgartner, R., Somorjai, R.L.: Using domain knowledge for in the random subspace method: Application: Application to the classification of biomedical spectra. In: Oza, et al. [95], pp. 336–345

    Google Scholar 

  100. Procopio, M.J., Kegelmeyer, W.P., Grudic, G.Z., Mulligan, J.: Terrain segmentation with on-line mixtures of experts for autonomous robot navigation. In: Benediktsson, et al. [12], pp. 385–397

    Google Scholar 

  101. Rahman, F., Tarnikova, Y., Kumar, A., Alam, H.: Second guessing a commercial ’black box’ classifier by an ’in house’ classifier: Serial classifier combination in a speech recognition application. In: Roli, et al. [109], pp. 374–383

    Google Scholar 

  102. Rajan, S., Ghosh, J.: An empirical comparison of hierarchical vs. two-level approaches to multiclass problems. In: Roli, et al. [109], pp. 283–292

    Google Scholar 

  103. Rajan, S., Ghosh, J.: Exploiting class hierarchies for knowledge transfer in hyperspectral data. In: Oza, et al. [95], pp. 417–427

    Google Scholar 

  104. Raudys, S., Baykan, Ö.K., Babalik, A., Denisov, V., Bielskis, A.A.: Classifiers fusion in recognition of wheat varieties. In: Haindl, et al. [59], pp. 62–71

    Google Scholar 

  105. Re, M., Valentini, G.: Ensemble based data fusion for gene function prediction. In: Benediktsson, et al. [12], pp. 448–457

    Google Scholar 

  106. Re, M., Valentini, G.: An experimental comparison of hierarchical bayes and true path rule ensembles for protein function prediction. In: Gayar, et al. [52], pp. 294–303

    Google Scholar 

  107. Roli, F., Kittler, J. (eds.): MCS 2002. LNCS, vol. 2364. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  108. Roli, F., Kittler, J., Fumera, G., Muntoni, D.: An experimental comparison of classifier fusion rules for multimodal personal identity verification systems. In: Roli, Kittler [107], pp. 325–336

    Google Scholar 

  109. Roli, F., Kittler, J., Windeatt, T. (eds.): MCS 2004. LNCS, vol. 3077. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  110. Sadeghi, M., Khoshrou, S., Kittler, J.: Confidence based gating of colour features for face authentication. In: Haindl, et al. [59], pp. 121–130

    Google Scholar 

  111. Samadzadegan, F., Bigdeli, B., Ramzi, P.: A multiple classifier system for classification of lidar remote sensing data using multi-class svm. In: Gayar, et al. [52], pp. 254–263

    Google Scholar 

  112. Sansone, C., Kittler, J., Roli, F. (eds.): MCS 2011. LNCS, vol. 6713. Springer, Heidelberg (2011)

    MATH  Google Scholar 

  113. Sansone, C., Paduano, V., Ceccarelli, M.: Combining 2d and 3d features to classify protein mutants in hela cells. In: Gayar, et al. [52], pp. 284–293

    Google Scholar 

  114. De Santo, M., Percannella, G., Sansone, C., Vento, M.: Combining audio-based and video-based shot classification systems for news videos segmentation. In: Oza, et al. [95], pp. 397–406

    Google Scholar 

  115. Schettini, R., Brambilla, C., Cusano, C.: Content-based classification of digital photos. In: Roli, Kittler [107], pp. 272–281

    Google Scholar 

  116. Seewald, A.K.: How to make stacking better and faster while also taking care of an unknown weakness. In: Sammut, C., Hoffmann, A.G. (eds.) Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, pp. 554–561. Morgan Kaufmann (2002)

    Google Scholar 

  117. Serrano, Á., de Diego, I.M., Conde, C., Cabello, E., Bai, L., Shen, L.: Fusion of support vector classifiers for parallel gabor methods applied to face verification. In: Haindl, et al. [59], pp. 141–150

    Google Scholar 

  118. Sirlantzis, K., Fairhurst, M.C., Hoque, S.: Genetic algorithms for multi-classifier system configuration: A case study in character recognition. In: Kittler, Roli [74], pp. 99–108

    Google Scholar 

  119. Sirlantzis, K., Hoque, S., Fairhurst, M.C.: Input space transformations for multi-classifier systems based on n-tuple classifiers with application to handwriting recognition. In: Windeatt, Roli [134], pp. 356–365

    Google Scholar 

  120. Slavík, P., Govindaraju, V.: Use of lexicon density in evaluating word recognizers. In: Kittler, Roli [73], pp. 310–319

    Google Scholar 

  121. Smits, P.C.: Combining supervised remote sensing image classifiers based on individual class performances. In: Kittler, Roli [74], pp. 269–278

    Google Scholar 

  122. Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler, Roli [73], pp. 52–66

    Google Scholar 

  123. Sun, S.: Ensemble learning methods for classifying eeg signals. In: Haindl, et al. [59], pp. 113–120

    Google Scholar 

  124. Sun, S.: An improved random subspace method and its application to eeg signal classification. In: Haindl, et al. [59], pp. 103–112

    Google Scholar 

  125. Svetnik, V., Liaw, A., Tong, C., Wang, T.: Application of breiman’s random forest to modeling structure-activity relationships of pharmaceutical molecules. In: Roli, et al. [109], pp. 334–343

    Google Scholar 

  126. Ting, K.M., Zhu, L.: Boosting support vector machines successfully. In: Benediktsson, et al. [12], pp. 509–518

    Google Scholar 

  127. Tulyakov, S., Govindaraju, V.: Neural network optimization for combinations in identification systems. In: Benediktsson, et al. [12], pp. 418–427

    Google Scholar 

  128. Visentini, I., Kittler, J., Foresti, G.L.: Diversity-based classifier selection for adaptive object tracking. In: Benediktsson, et al. [12], pp. 438–447

    Google Scholar 

  129. Wan, W., Fraser, D.: A multiple self-organizing map scheme for remote sensing classification. In: Kittler, Roli [73], pp. 300–309

    Google Scholar 

  130. Wang, X., Tang, X.: Experimental study on multiple lda classifier combination for high dimensional data classification. In: Roli, et al. [109], pp. 344–353

    Google Scholar 

  131. Waske, B., Benediktsson, J.A., Sveinsson, J.R.: Classifying remote sensing data with support vector machines and imbalanced training data. In: Benediktsson, et al. [12], pp. 375–384

    Google Scholar 

  132. Webb, G.I.: Multiboosting: A technique for combining boosting and wagging. Machine Learning 40(2), 159–196 (2000)

    Article  Google Scholar 

  133. Wilczok, E., Lellmann, W.: Design and evaluation of an adaptive combination framework for ocr result strings. In: Windeatt, Roli [134], pp. 395–404

    Google Scholar 

  134. Windeatt, T., Roli, F. (eds.): MCS 2003. LNCS, vol. 2709. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  135. Windridge, D., Bowden, R.: Induced decision fusion in automated sign language interpretation: Using ica to isolate the underlying components of sign. In: Roli, et al. [109], pp. 303–313

    Google Scholar 

  136. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  137. Witten, I.H., Frank, E., Hal, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)

    Google Scholar 

  138. Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)

    Article  Google Scholar 

  139. Xu, J.-W., Singh, V., Govindaraju, V., Neogi, D.: A cascade multiple classifier system for document categorization. In: Benediktsson, et al. [12], pp. 458–467

    Google Scholar 

  140. Yousri, N.A.: A multi-objective sequential ensemble for cluster structure analysis and visualization and application to gene expression. In: Gayar, et al. [52], pp. 274–283

    Google Scholar 

  141. Zhang, C.-X., Duin, R.P.W.: An empirical study of a linear regression combiner on multi-class data sets. In: Benediktsson, et al. [12], pp. 478–487

    Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli “Federico II”, Napoli, Italy

    Francesco Gargiulo, Claudio Mazzariello & Carlo Sansone

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  1. Francesco Gargiulo
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  2. Claudio Mazzariello
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  3. Carlo Sansone
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Correspondence to Francesco Gargiulo .

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  1. , Dipto. Ingegneria dell'Informazione, Università degli Studi di Siena, Via Roma 56, Siena, 53100, Italy

    Monica Bianchini

  2. Fac. Ingegneria, Dipto. Ingegneria dell'Informazione, Università Siena, Via Roma 56, Siena, 53100, Italy

    Marco Maggini

  3. University of Canberra, School of Electrical and Information, Adjunct Professor, Mawson Lakes Campus, ACT, 2601, Australia

    Lakhmi C. Jain

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Gargiulo, F., Mazzariello, C., Sansone, C. (2013). Multiple Classifier Systems: Theory, Applications and Tools. In: Bianchini, M., Maggini, M., Jain, L. (eds) Handbook on Neural Information Processing. Intelligent Systems Reference Library, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36657-4_10

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