Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Integrating Map-Reduce and Stream-Processing for Efficiency (MRSP) Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Adjusting Parameters of the Classifiers in Multiclass Classification

verfasst von : Daniel Kostrzewa, Robert Brzeski

Erschienen in: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The article presents the results of the optimization process of classification for five selected data sets. These data sets contain the data for the realization of the multiclass classification. The article presents the results of initial classification, carried out by dozens of classifiers, as well as the results after the process of adjusting parameters, this time obtained for a set of selected classifiers. At the end of article, a summary and the possibility of further work are provided.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Attribute Reduction in a Dispersed Decision-Making System with Negotiations
Nächstes Kapitel Data Mining - A Tool for Migration Stock Prediction
Literatur
1.
2.
3.
4.
Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)CrossRef
5.
Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)MATH
6.
Aly, M.: Survey on multiclass classification methods, Technical report, Caltech (2005)
7.
Arie, B.D.: Comparison of classification accuracy using cohen’s weighted kappa. Expert Syst. Appl. 34(2), 825–832 (2008)CrossRef
8.
Bach, M., Werner, A., Zywiec, J., Pluskiewicz, W.: The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis. Inf. Sci. Life Sci. Data Anal. 381, 174–190 (2016)
9.
Costa, E., Lorena, A., Carvalho, A., Freitas, A.: A review of performance evaluation measures for hierarchical classifiers. In: Evaluation Methods for Machine Learning II, AAAI 2007 Workshop, pp. 182–196. AAAI Press (2007)
10.
Cuturi, M.: Fast global alignment kernels. In: Proceedings of the International Conference on Machine Learning (2011)
11.
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH
12.
Freeman, E., Moisen, G.: A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol. Model. 217(1–2), 48–58 (2008)CrossRef
13.
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Stanford University, Stanford (1998)MATH
14.
Garris, M., Blue, J., Candela, G., Dimmick, D., Geist, J., Grother, P., Janet, S., Wilson, C.: NIST form-based handprint recognition system. NISTIR 5469 (1994)
15.
Haiyang, Z.: A short introduction to data mining and its applications (2011)
16.
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106 (2001)
17.
John, G., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345, San Mateo (1995)
18.
Johnson, B.: High resolution urban land cover classification using a competitive multi-scale object-based approach. Remote Sens. Lett. 4(2), 131–140 (2013)CrossRef
19.
Johnson, B., Xie, Z.: Classifying a high resolution image of an urban area using super-object information. ISPRS J. Photogrammetry Remote Sens. 83, 40–49 (2013)CrossRef
20.
Josinski, H., Kostrzewa, D., Michalczuk, A., Switonski, A.: The exIWO metaheuristic for solving continuous and discrete optimization problems. Sci. World J. 2014 (2014). 14 p. doi:10.​1155/​2014/​831691. Article ID 831691
21.
Świtoński, A., Polański, A., Wojciechowski, K.: Human identification based on gait paths. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 531–542. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-23687-7_​48 CrossRef
22.
Kasprowski, P., Harezlak, K.: Using dissimilarity matrix for eye movement biometrics with a jumping point experiment. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2016. SIST, vol. 57, pp. 83–93. Springer, Cham (2016). doi:10.​1007/​978-3-319-39627-9_​8 CrossRef
23.
Kostrzewa, D., Josinski, H.: The exIWO metaheuristic - a recapitulation of the research on the join ordering problem. Commun. Comput. Inf. Sci. 424, 10–19 (2014)
24.
Lessmanna, S., Baesens, B., Seowd, H.V., Thomasc, L.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 124–136 (2015)CrossRef
25.
Mehra, N., Gupta, S.: Survey on multiclass classification methods. Int. J. Comput. Sci. Inf. Technol. 4(4), 572–576 (2013)
26.
Mehrabian, A., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)CrossRef
27.
Pahlavani, P., Delavar, M., Frank, A.: Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem. Int. J. Appl. Earth Obs. Geoinf. 18, 313–328 (2012)CrossRef
28.
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning (1998)
29.
Powers, D.: Evaluation: from precision, recall and f-score to roc, informedness, markedness & correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)
30.
Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing classifiers. In: Proceedings of the ICML 1998, pp. 445–453. Morgan Kaufmann, San Francisco (1998)
31.
Ramaswamy, S., Tamayo, P., Rifkin, R.S.M., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J., Poggio, T., Gerald, W., Loda, M., Lander, E., Golub, T.: Multiclass cancer diagnosis using tumor gene expression signatures. PNAS 98(26), 15149–15154 (2001)CrossRef
32.
Reyes-Ortiz, J.L., Oneto, L., Sama, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)CrossRef
33.
Smith, M., Martinez, T.: Improving classification accuracy by identifying and removing instances that should be misclassified. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 2690–2697 (2011)
34.
Sumner, M., Frank, E., Hall, M.: Speeding up logistic model tree induction. In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 675–683 (2005)
35.
Unler, A., Murat, A.: A discrete particle swarm optimization method for feature selection in binary classification problems. Eur. J. Oper. Res. 206(3), 528–539 (2010)MATHCrossRef
36.
Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B., Yu, P., Zhou, Z.H., Steinbach, M., Hand, D., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2008)CrossRef
Metadaten
Titel
Adjusting Parameters of the Classifiers in Multiclass Classification
verfasst von
Daniel Kostrzewa
Robert Brzeski
Copyright-Jahr
2017
Buch
Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Print ISBN: 978-3-319-58273-3

Electronic ISBN: 978-3-319-58274-0

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-58274-0_8