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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)

Abstract

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.

Keywords

LogitBoost algorithm Distance to the decision boundary Score function 

Notes

Acknowledgments

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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