The efficient and effective process of extracting the useful information from high-dimensional data is a worth studying problem. The high-dimensional data is a big and complex that it becomes difficult to be processed and classified. Dimensionality reduction (DR) is an important and a key method to address these problems. This paper presents a hybrid approach for data classification constituted from the combination of principal component analysis (PCA) and enhanced extreme learning machine (EELM). The proposed approach has two basic components. Firstly, PCA; as a linear data reduction, is implemented to reduce the number of dimensions by removing irrelevant attributes to speed up the classification method and to minimize the complexity of computation. Secondly, EELM is performed by modifying the activation function of single hidden layer feedforward neural network (SLFN) perfect distribution of categories. The proposed approach depends on a static determination of the reduced number of principal components. The proposed approach is applied on several datasets and is assisted its effectiveness by performing different experiments. For more reliability, the proposed approach is compared with two of the previous works, which used PCA and ELM in data analysis. |
