A new variant Boosting algorithm: Update sample’s weight according to standard deviation of Error-Right statistics
Cheng-Li Chen · Journal of Central South University(Science and Technology) · 2012
Traditional AdaBoost algorithm sets difficult samples with too much weight and the over weighted difficult samples will lead to a declination of the ensemble performance,therefore,a new variant of AdaBoost called ERstd-AdaBoost algorithm were proposed.The experiments on several benchmark real-world sets available from the UCI repository were carried out.The results show that the strategy can update the weight of samples in the fixed stage,that is,it controls the increase of weight of repetitions misclassified samples.ERstd-AdaBoost algorithm can avoid setting misclassified samples too much weight which would lead to a declination of the ensemble performance.Hence,this new strategy can update difficult sample’s weight to achieve a better accuracy with no decline of diversity.The new algorithm can absolutely improve the performance of AdaBoost,and its stabilization is acceptable.