Learning Rule for Linear Multilayer Feedforward ANN by Boosted Decision Stumps

  • Mirza Mubasher BaigEmail author
  • El-Sayed M. El-Alfy
  • Mian M. Awais
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)


A novel method for learning a linear multilayer feedforward artificial neural network (ANN) by using ensembles of boosted decision stumps is presented. Network parameters are adapted through a layer-wise iterative traversal of neurons with weights of each neuron learned by using a boosting based ensemble and an appropriate reduction. Performances of several neural network models using the proposed method are compared for a variety of datasets with networks learned using three other algorithms, namely Perceptron learning rule, gradient decent back propagation algorithm, and Boostron learning.


  1. 1.
    Baig, M.M., Awais, M.M., El-Alfy, E.-S.M.: BOOSTRON: boosting based perceptron learning. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 199–206. Springer, Heidelberg (2014) Google Scholar
  2. 2.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2(1), 263–286 (1995)zbMATHGoogle Scholar
  3. 3.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Ann. Stat. 26(2), 451–471 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Iba, W., Langley, P.: Induction of one-level decision trees. In: Proceedings of the 9th International Conference on Machine Learning, pp. 233–240 (1992)Google Scholar
  5. 5.
    Lichman, M.: UCI machine learning repository (2013).
  6. 6.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Rumelhart, D.E., McClelland, J.L., PDP Research Group.: Parallel Distributed Processing: Explorations in the Microstructures of Cognition, vol. 1. MIT Press, Cambridge, MA, USA (1986)Google Scholar
  8. 8.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mirza Mubasher Baig
    • 1
    Email author
  • El-Sayed M. El-Alfy
    • 2
  • Mian M. Awais
    • 1
  1. 1.School of Science and Engineering (SSE)Lahore University of Management Sciences (LUMS)LahorePakistan
  2. 2.College of Computer Sciences and EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

Personalised recommendations