New AdaBoost Algorithm Based on Interval-Valued Fuzzy Sets
This paper presents a new extension of AdaBoost algorithm based on interval-valued fuzzy sets. This extension is for the weights used in samples of the training sets. The original weights are the real number from the interval [0, 1]. In our approach the weights are represented by the interval-valued fuzzy set, that is any weight has a lower and upper membership function. The same value of lower and upper membership function has a weight of the appropriate weak classifier. In our study we use the boosting by the reweighting method where each weak classifier is based on the recursive partitioning method. The described algorithm was tested on two generation data sets and two sets from UCI repository. The obtained results are compared with the original AdaBoost algorithm.
KeywordsAdaBoost algorithm interval-valued fuzzy sets
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