An efficient classification based on genetically optimised hybrid PCA-Kernel ELM learning
Tripti Goel, Vijay Nehra, Virendra Prasad Vishwakarma · International Journal of Applied Pattern Recognition · 2016
A new classifier based on genetically optimised kernel extreme learning machine (KELM) is presented here. Firstly, principal component analysis (PCA) is used to retrieve the important features from the datasets and further these features are used for classification. Classification is done by using the genetically optimised KELM algorithm in which the kernel parameters of the kernel function of ELM are optimised by using the genetic algorithm (GA). The present approach is investigated on eight benchmark biomedical datasets from UCI machine learning repository and AT&T face database to show its efficiency and effectiveness. The results are validated by using classification accuracy, ROC and cross validation. The results show that the proposed learning algorithm is better in terms of generalisation performance and learning speed compared to other state of the art learning algorithms.