Modified Score Function and Linear Weak Classifiers in LogitBoost Algorithm

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1062)


This paper presents a new extension of LogitBoost algorithm based on the distance of the object to the decision boundary, which is defined by the weak classifier used in boosting. In the proposed approach this distance is transformed by Gaussian function and defines the value of a score function. The assumed form of transforming functions means that the objects closest or farthest located from the decision boundary of the basic classifier have the lowest value of the scoring function. The described algorithm was tested on four data sets from UCI repository and compared with LogitBoost algorithm.


LogitBoost algorithm Distance to the decision boundary Score function 



This work was supported in part by the National Science Centre, Poland under the grant no. 2017/25/B/ST6/01750.


  1. 1.
    Burduk, R.: The AdaBoost algorithm with the imprecision determine the weights of the observations. In: Asian Conference on Intelligent Information and Database Systems, pp. 110–116. Springer (2014)Google Scholar
  2. 2.
    Dmitrienko, A., Chuang-Stein, C., D’Agostino, R.B.: Pharmaceutical Statistics Using SAS: A Practical Guide. SAS Institute (2007)Google Scholar
  3. 3.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: Applying image features and AdaBoost classification for vehicle detection in the SM4Public system. In: Image Processing and Communications Challenges 7, pp. 81–88. Springer (2016)Google Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156. Citeseer (1996)Google Scholar
  6. 6.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)zbMATHGoogle Scholar
  7. 7.
    Kearns, M., Valiant, L.: Cryptographic limitations on learning boolean formulae and finite automata. J. ACM (JACM) 41(1), 67–95 (1994)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kozik, R., Choraś, M.: The http content segmentation method combined with AdaBoost classifier for web-layer anomaly detection system. In: International Joint Conference SOCO 2016-CISIS 2016-ICEUTE 2016, pp. 555–563. Springer (2016)Google Scholar
  9. 9.
    Oza, N.C.: Boosting with averaged weight vectors. In: International Workshop on Multiple Classifier Systems, pp. 15–24. Springer (2003)Google Scholar
  10. 10.
    Rejer, I.: Genetic algorithms for feature selection for brain-computer interface. Int. J. Pattern Recognit Artif Intell. 29(05), 1559008 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Shen, C., Li, H.: On the dual formulation of boosting algorithms. IEEE Transact. Pattern Anal. Mach. Intell. 32(12), 2216–2231 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Szenkovits, A., Meszlényi, R., Buza, K., Gaskó, N., Lung, R.I., Suciu, M.: Feature selection with a genetic algorithm for classification of brain imaging data. In: Advances in Feature Selection for Data and Pattern Recognition, pp. 185–202. Springer (2018)Google Scholar
  13. 13.
    Topolski, M.: Algorithm of multidimensional analysis of main features of PCA with blurry observation of facility features detection of carcinoma cells multiple myeloma. In: International Conference on Computer Recognition Systems, pp. 286–294. Springer (2019)Google Scholar
  14. 14.
    Wozniak, M.: Proposition of boosting algorithm for probabilistic decision support system. In: International Conference on Computational Science, pp. 675–678. Springer (2004)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of ElectronicWroclaw University of Science and TechnologyWroclawPoland

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