According to the auxiliary training data with large redundancy and imbalance between positive and negative samples

an improved integrated transfer learning algorithmic -The Unbalanced Integrated Transfer Learning Algorithmic is proposed.Applied these auxiliary training data to transfer and help classifying on target data.New sample initialization and regulation weight method highlighted negative sample identification ability.Through dynamic adjusting auxiliary training set

eliminated redundant data according to the weight lower threshold

reduced their influence on the classifier and improved the transfer learnings performance.Experimental results on the actual bridge monitoring data show that this algorithmic is advanced than TrAdaboost.