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.
CITATION STYLE
Burduk, R., & Bozejko, W. (2019). Gentle AdaBoost algorithm with score function dependent on the distance to decision boundary. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11703 LNCS, pp. 303–310). Springer Verlag. https://doi.org/10.1007/978-3-030-28957-7_25
Mendeley helps you to discover research relevant for your work.