A Voting-Near-Extreme-Learning-Machine Classification Algorithm
Hui-Rang Hou, Qing‐Hao Meng, Xiao-Nei Zhang · 2018
For the classifiaction tasks within two classes, a feature extraction method combining the principal component analysis (PCA) and the linear discriminant analysis (LDA) is adopted, and an improved extreme learning machine (ELM), i.e., the near extreme learning machine (NELM classification algorithm), is presented. To further improve the classification performance, a voting-NELM (VNELM) is proposed. To examine the performance of our proposed classification algorithm, two different tests were carried out: slow cortical potential (SCP) signal classification and Chinese liquor (true or false) recognition. Experimental results reveal that for the SCP signal classification using the BCI competition II dataset Ia, an accuracy of 93.52% is obtained through the VNELM algorithm, better than that (92.30%) of the state-of-the-art method (i.e., the best improved ELM algorithm, V-ELM). When applied the VNELM algorithm to the Chinese liquor recognition, all the single-sensor-based classification results are better than that of the ELM and the V-ELM, and a better average accuracy of 99.25% is obtained based on the multi-sensor response signals, increasing the accuracy by 26% and 6.25% from that (73.25% and 93.00%) of the ELM and the V-ELM, respectively.