This paper gives a research on IB(inverse Boosting)algorithm and proposes an improved version of IB called IB+.Both IB and IB+ algorithm will enhance the weight of samples which have been classified correctly during the training process.The most difference between IB and IB+ is the method to update the weight of training samples in each iteration.For IB algorithm

the weight of training samples will be updated according to an inverse error vector which was decided by the performance of the last trained single net.However the IB+ algorithm adopts a mesosphere ensemble net instead of a single net to determine the inverse error vector thus a more suitable sample distribution will be achieved.Further experiment results show that the performance of ensemble net which was developed using an inverse error vector to create new sample distribution will be decided by the performance of base single net not the degree of correlation.