Abstract
This paper presents a new extension of Gentle AdaBoost 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 Gentle AdaBoost algorithm.
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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. (2019). Gentle AdaBoost Algorithm with Score Function Dependent on the Distance to Decision Boundary. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_25
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DOI: https://doi.org/10.1007/978-3-030-28957-7_25
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