关键词: AdaBoost分类器, Ransac算法, 样本加权, 分类

Abstract: Taking this reason that AdaBoost is sensitive to outliers, robust AdaBoost classifier is constructed against outlier interference using Ransac. Different from the other weak classifier sample weighting and controlling method in AdaBoost, Ransac is employed and introduced to the process of classifier model construction to overcome the drawbacks of the existing AdaBoost weak classifier weighting algorithms. Meanwhile, the adverse affection of outliers can be effectively eliminated by virtue of the Ransac algorithm’s strong ability in removing outliers. Through above strategy, the classifier degradation is able to be avoided. Finally, in the validation experiment, the designed classifier model is applied in the handwriting samples classification including some outliers. The experimental results show its validity.

Key words: AdaBoost classifier, Ransac algorithm, sample weighting, classification