دستگاه یادگیری افراطی انفرادی از طریق انتگرال فازی برای طبقه بندی داده ها

Extreme learning machine (ELM) is a simple but efficient algorithm for training single hidden layer feed-forward neural networks (SLFNs) with fast speed and good generalization ability. ELM has been successfully applied to many fields, such as pattern recognition, computer vision, biological information processing, etc. However, there are two problems in ELM. the first one is architecture selection, the second one is prediction instability. In order to deal with the two problems, based on dropout technique, an ensemble learning method is proposed in this paper. The proposed method can solve the first problem and can improve prediction stability. Our experimental results and statistical analysis on 14 data sets confirm this conclusion. Furthermore, our experimental results also show that the proposed approach outperforms the original ELM on prediction stability and classification accuracy.