Large-scale data mining has become a very difficult issue using traditional methods
because the data complexity is very high. In the proposed approach, an integration of three
methods; Optimised Principal Component Analysis (OPCA), Optimised Enhanced Extreme
Learning Machine (OEELM), and stratified sampling, called OPCA-EELM2SS, is presented to
provide intelligent and enhanced large-scale data mining. OPCA provides a good representation
of large-scale data sets by using the Stratified Sample (SS). By using OEELM, the optimal
number of Hidden Nodes (HNs) in ELM is exploited to build a single hidden layer feedforward
neural network (SLFN). The proposed approach is tested by using nineteen benchmark data sets.
The experimental results demonstrate the effectiveness of the proposed approach by performing
different experiments for classical PCA and Independent Component Analysis (ICA), which are
integrated with the enhanced ELM using different evaluation criteria. For more reliability, the
proposed approach is compared with many previous methods. |
