Authors: Doaa L. El-Bably; Khaled M. Fouad
Addresses: Department of Scientific Computing, Faculty of Computers and Informatics, Benha University, Egypt ' Department of Information Systems, Faculty of Computers and Informatics, Benha University, Egypt
Abstract: The efficient and effective process of extracting the useful information from high-dimensional data is worth studying. High-dimensional data is big and complex and that it becomes difficult process and classify. Dimensionality reduction (DR) is important and the 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 minimise the complexity of computation. Secondly, EELM is performed by modifying the activation function of single hidden layer feedforward neural network (SLFN) for the 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 its effectiveness is supported by the different experiments performed. For more reliability, the proposed approach is compared with two of the previous works, which used PCA and ELM in data analysis.
Keywords: data mining; data classification; PCA; neural network; enhanced ELM.
International Journal of Advanced Intelligence Paradigms, 2022 Vol.23 No.3/4, pp.217 - 247
Received: 13 Jul 2017
Accepted: 05 Sep 2017
Published online: 03 Nov 2022 *