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
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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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Burduk, R., Bozejko, W. (2020). Modified Score Function and Linear Weak Classifiers in LogitBoost Algorithm. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_7
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DOI: https://doi.org/10.1007/978-3-030-31254-1_7
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