Advertisement

Improved Multilabel Classification with Neural Networks

  • Rafał Grodzicki
  • Jacek Mańdziuk
  • Lipo Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

This paper considers the multilabel classification problem, which is a generalization of traditional two-class or multi-class classification problem. In multilabel classification a set of labels (categories) is given and each training instance is associated with a subset of this label-set. The task is to output the appropriate subset of labels (generally of unknown size) for a given, unknown testing instance. Some improvements to the existing neural network multilabel classification algorithm, named BP-MLL, are proposed here. The modifications concern the form of the global error function used in BP-MLL. The modified classification system is tested in the domain of functional genomics, on the yeast genome data set. Experimental results show that proposed modifications visibly improve the performance of the neural network based multilabel classifier. The results are statistically significant.

Keywords

multilabel learning system neural network backpropagation bioinformatics functional genomics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, M.L., Zhou, Z.H.: Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization. IEEE Transactions on Knowledge and Data Engineering 18(10), 1338–1351 (2006)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Elissef, A., Weston, J.: A Kernel Method for Multi-Labelled Classification. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 681–687 (2002)Google Scholar
  4. 4.
    Clare, A., King, R.D.: Knowledge Discovery in Multi-Label Phenotype Data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Pavlidis, P., Weston, J., Cai, J., Grundy, W.N.: Combining Microarray Expression Data and Phylogenetic Profiles to Learn Functional Categories using Support Vector Machines. In: 5th Annual International Conference Computational Molecular Biology (RECOMB 2001), pp. 242–248 (2001)Google Scholar
  6. 6.
    McCallum, A.: Multi-Label Text Classification with a Mixture Model Trained by EM. In: Working Notes Am. Assoc. Artificial Intelligence Workshop Text Learning (AAAI 1999) (1999)Google Scholar
  7. 7.
    Schapire, R.E., Singer, Y.: BoosTexter: A Boosting-Based System for Text Categorization. Machine Learning 39(2/3), 135–168 (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kazawa, H., Izumitani, T., Taira, H., Maeda, E.: Maximal Margin Labeling for Multi-Topic Text Categorization. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 649–656 (2005)Google Scholar
  9. 9.
    Werbos, P.J.: Beyond Regression: New Tools for Prediction and Anlysis in the Behavioral Sciences. PhD thesis, Harvard University (1974)Google Scholar
  10. 10.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362 (1986)Google Scholar
  11. 11.
    Comite, F.D., Gilleron, R., Tommasi, M.: Learning Multi-Label Alternating Decision Tree from Texts and Data. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 35–49. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rafał Grodzicki
    • 1
  • Jacek Mańdziuk
    • 1
  • Lipo Wang
    • 2
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarszawaPoland
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

Personalised recommendations