关键词: AdaBoost算法, 支持向量机, 损失函数

Abstract: As to the issue that the weight expansion for hardest samples can cause imbalance when updating the training sample in AdaBoost algorithm, an improved approach based on the Loss Function (LF) of the different patterns, namely, LF-AdaBoost, was proposed. The weight tuning was affected not only by the training error, but the performance of base classifiers for different classes, thus avoiding the excessive concentration phenomenon. The results based on UCI data sets and different base classifiers have shown that the approach can improve the speed of convergence and overcome the imbalance, as well as promote the generalization ability of ensemble classifier.

Key words: AdaBoost algorithm, Support Vector Machine (SVM), Loss Function (LF)