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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Recent Advances in Recommender Systems and Future Directions Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Two Stage SVM and kNN Text Documents Classifier

verfasst von : Marcin Kępa, Julian Szymański

Erschienen in: Pattern Recognition and Machine Intelligence

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

The paper presents an approach to the large scale text documents classification problem in parallel environments. A two stage classifier is proposed, based on a combination of k-nearest neighbors and support vector machines classification methods. The details of the classifier and the parallelisation of classification, learning and prediction phases are described. The classifier makes use of our method named one-vs-near. It is an extension of the one-vs-all approach, typically used with binary classifiers in order to solve multiclass problems. The experiments were performed on a large scale dataset, with use of many parallel threads on a supercomputer. Results of the experiments show that the proposed classifier scales well and gives reasonable quality results. Finally, it is shown that the proposed method gives better performance compared to the traditional approach.

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 Big Data Processing by Volunteer Computing Supported by Intelligent Agents
Nächstes Kapitel Task Allocation and Scalability Evaluation for Real-Time Multimedia Processing in a Cluster Environment
Literatur
1.
2.
Gantner, Z., Lars, S.-T.: Automatic content-based categorization of wikipedia articles. In: Proceedings of the 2009 Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources, People’s Web 2009, pp. 32–37. Association for Computational Linguistics, Stroudsburg (2009)
3.
Han, E.-H.S., Karypis, G.: Centroid-based document classification: analysis and experimental results. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 424–431. Springer, Heidelberg (2000) CrossRef
4.
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATH
5.
Szymański, J.: Wikipedia articles representation with matrix’u. In: Hota, C., Srimani, P.K. (eds.) ICDCIT 2013. LNCS, vol. 7753, pp. 500–510. Springer, Heidelberg (2013) CrossRef
6.
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967)MATHCrossRef
7.
Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems 18, pp. 1473–1480. MIT Press, Cambridge (2005)
8.
Draszawka, K., Szymanski, J.: Thresholding strategies for large scale multi-label text classifier. In: IEEE 2013 the 6th International Conference on Human System Interaction (HSI), pp. 350–355 (2013)
9.
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
10.
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2, 265–292 (2002)MATH
11.
Duan, K.-B., Keerthi, S.S.: Which is the best multiclass SVM method? An empirical study. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 278–285. Springer, Heidelberg (2005) CrossRef
12.
Vinoth, R., Jayachandran, A., Balaji, M., Srinivasan, R.: A hybrid text classification approach using KNN and SVM. Int. J. Adv. Found. Res. Comput. (IJAFRC) 1(3), 20–26 (2014)
13.
Zhang, H., Berg, A., Maire, M., Malik, J.: SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: Proceedinngs of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2126–2136 (2006)
14.
Shih, Y., Wei, D.: Machine learning final project: Handwritten sanskrit recognition using a multi-class SVM with K-NN guidance (2011)
15.
Hsu, C.-C., Yang, C.-Y., Yang, J.-S.: Associating kNN and SVM for higher classification accuracy. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 550–555. Springer, Heidelberg (2005) CrossRef
16.
Balicki, J., Szymanski, J., Kępa, M., Draszawka, K., Korlub, W.: Improving effectiveness of svm classifier for large scale data. In: Proceeedings of the 14th International Conference on Artificial Intelligence and Soft Computing (in print). Springer (2015)
17.
Gabriel, E., et al.: Open MPI: goals, concept, and design of a next generation mpi implementation. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J. (eds.) EuroPVM/MPI 2004. LNCS, vol. 3241, pp. 97–104. Springer, Heidelberg (2004) CrossRef
18.
19.
Szymanski, J.: Comparative analysis of text representation methods using classification. Cybern. Syst. 45, 180–199 (2014)CrossRef
20.
Shanahan, J.G., Roma, N.: Improving SVM text classification performance through threshold adjustment. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 361–372. Springer, Heidelberg (2003) CrossRef
21.
22.
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University (2003)
23.
Szymański, J.: Mining relations between wikipedia categories. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds.) NDT 2010. CCIS, vol. 88, pp. 248–255. Springer, Heidelberg (2010) CrossRef
24.
Czarnul, P.: Modeling, run-time optimization and execution of distributed workflow applications in the JEE-based beesycluster environment. J. Supercomput. 63(1), 1–26 (2010)
Metadaten
Titel
Two Stage SVM and kNN Text Documents Classifier
verfasst von
Marcin Kępa
Julian Szymański
Copyright-Jahr
2015
Buch
Pattern Recognition and Machine Intelligence

Print ISBN: 978-3-319-19940-5

Electronic ISBN: 978-3-319-19941-2

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-19941-2_27

Premium Partner

    Bildnachweise