In order to solve the problem that parameter random assignment reduces the robustness of the algorithm and the performance is significantly affected by noise of Extreme Learning Machine (ELM), combining Denoising AutoEncoder (DAE) with ELM algorithm, a DAE based ELM (DAE-ELM) algorithm was proposed. Firstly, a denoising autoencoder was used to generate the input data, input weight and hidden layer parameters of ELM. Then, the hidden layer output was obtained through ELM to complete the training of classifier. On the one hand, the advantages of DAE were inherited by the algorithm, which means the features extracted automatically were more representative and robust and were impervious to noise. On the other hand, the randomness of parameter assignment of ELM was overcome and the robustness of the algorithm was improved. The experimental results show that, compared to ELM, Principal Component Analysis ELM (PCA-ELM), SAA-2, the classification error rate of DAE-ELM at least decreases 5.6% on MNIST, 3.0% on Fashion MINIST, 2.0% on Rectangles and 12.7% on Convex.

Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) is a multi-class AdaBoost algorithm. To further improve the performance of SAMME, the influence of using weighed error rate and pseudo loss on SAMME algorithm was studied, and a dynamic weighted Adaptive Boosting (AdaBoost) algorithm named SAMME with Resampling and Dynamic weighting (SAMME.RD) algorithm was proposed based on the classification of sample's effective neighborhood area by using the base classifier. Firstly, it was determined that whether to use weighted probability and pseudo loss or not. Then, the effective neighborhood area of sample to be tested in the training set was found out. Finally, the weighted coefficient of the base classifier was determined according to the classification result of the effective neighborhood area based on the base classifier. The experimental results show that, the effect of calculating the weighted coefficient of the base classifier by using real error rate is better. The performance of selecting base classifier by using real probability is better when the dataset has less classes and its distribution is balanced. The performance of selecting base classifier by using weighed probability is better when the dataset has more classes and its distribution is imbalanced. The proposed SAMME.RD algorithm can improve the multi-class classification accuracy of AdaBoost algorithm effectively.